Visible-thermal tiny pedestrian detection in UAV aerial images is crucial for online decision-making in urban security and disaster response. However, the extremely small scale and sparse distribution of pedestrians cause discriminative cues to be submerged by dominant low-frequency background and contextual redundancy during feature learning. Meanwhile, cross-modal spatial misalignment and spatially varying modality reliability hinder stable fine-grained correspondence, thereby degrading fusion quality. To address these issues, QA2FDet is proposed as a quality-aware adaptive alignment fusion network comprising three modules: spectrum-spatial decoupled enhancement module (SDE), cross-modal correspondence mining module (CCM), and prior-informed gated fusion (PGF). SDE leverages the discrete cosine transform to disentangle redundant low-frequency background information, while deep semantic gating propagates high signal-to-noise ratio details into shallow representations to enhance subtle cues of tiny pedestrians and suppress high-frequency noise. To establish fine-grained neighborhood correspondences under slight spatial offsets, thermal-guided local asymmetric cross-attention is designed in CCM. Finally, region-level quality and modality discrepancy are jointly modeled for adaptive cross-modal fusion in PGF. Extensive experiments on multiple UAV-based RGBT detection benchmarks demonstrate that QA2FDet achieves state-of-the-art performance and exhibits strong robustness in challenging aerial scenes.
Insects inhabit complex vibroscapes shaped by substrate-borne vibrations from multiple biotic and abiotic sources. One underappreciated topic is how vibrations function in predator-prey interactions. Tiny warty birch caterpillars (Falcaria bilineata) are known to produce complex vibratory signals to defend leaf-tip territories against conspecifics, raising the question of whether vibratory signalling and sensing also play roles in predator-prey interactions. We staged encounters between resident neonate caterpillars and three natural intruders: conspecifics, ladybird beetle larvae and adult ladybird beetles, while simultaneously recording behaviour and substrate-borne vibrations. Resident caterpillars showed three key responses - vibratory signalling, freezing and dropping - but these responses varied strongly with intruder identity and stage of encounter. Residents signalled vigorously toward conspecifics, with rates escalating as intruders approached their territories. In contrast, encounters with predators evoked predator-specific defensive strategies including freezing and dropping. Adult ladybird beetles, which caused high mortality (43%), elicited rapid escape responses and subsequent territory abandonment, while ladybird beetle larvae, which caused no mortality, triggered slower responses with initial signalling that ceased upon closer approach. Critically, vibrations generated by the 'footsteps' of each approaching intruder type produced distinct vibratory 'signatures', differing in amplitude, spectral and temporal characteristics. Resident caterpillars also initiated defensive responses before physical contact, often when intruders were still centimetres away. Together, these findings demonstrate that these miniature larvae, no larger than ∼1-2 mm, thrive in complex vibroscapes where vibrations not only function to advertise territory ownership against conspecifics but also provide essential early-warning cues enabling sophisticated threat assessment and context-appropriate defensive responses in predator-rich environments.
With improving survival, periviable neonates (≤25 weeks' gestation) represent a dynamic, but under-studied population in neonatal care, with persistently high cardiopulmonary and vascular vulnerability. Immature cardiovascular structure and function as a consequence of immature myocardial architecture, altered calcium handling, relative adrenal insufficiency, and persistent fetal shunts contribute to complex and dynamic cardiovascular physiology in this population. This may be present clinically in the form of hypotension and low end-organ perfusion. Traditional paradigms of blood pressure-based definitions of hypotension are poorly validated in this population and do not accurately reflect systemic blood flow or end-organ perfusion. Emerging evidence supports a phenotype-based multi-parametric approach to cardiovascular assessment, to distinguish ongoing physiological changes such as ductal physiology, pulmonary hypertension, low systemic vascular resistance and primary myocardial dysfunction phenotypes. However, significant knowledge gaps remain, including lack of normative hemodynamic values, and limited evidence guiding pharmacologic therapies. This narrative review focuses on the cardiovascular challenges in the management of periviable neonates as they transition to extrauterine life, delineating cardiac phenotypes, describing modalities of cardiovascular assessment and identifying existing knowledge gaps. We propose a physiology-based approach to cardiovascular management strategies based on existing, albeit limited, evidence. IMPACT: Periviable neonates present unique hemodynamic challenges due to structural and functional cardiovascular system immaturity, which can be categorized into different hemodynamic phenotypes dictated by baseline cardiac function, lung compliance and directionality of intracardiac shunts, especially the patent ductus arteriosus. In the absence of established normative reference values for common modalities of cardiac assessment, optimal care should consist of early identification of cardiac phenotypes, continuous surveillance, physiology-based management strategies, and frequent reassessment to guide individualized treatment.
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Microbial biodiversity is essential for the proper functioning and balance in the ecosystem, and they possess numerous biotechnological and industrial applications. Microbial biobanks serve as a major resource for conserving valuable microbial diversity, providing access for education, research, and industrial applications. The present comprehensive review article discusses the history, responsibilities, and applications of microbial biobanks, covering different regulatory ecosystem and their guidelines for the governance of biobanks. It specifically provides the current global status of MCCs and precisely sheds light on MCCs in India. This review critically highlights a stark disparity in the bio-resources management, showing dominance of Asia with substantial investment in bio-infrastructure. In India, a significant geographical disparity was observed with a heavy concentration of culture collections in states with established research infrastructure. Despite the immense commercial potential of microbes, very few MCCs are held by the private sector globally, leaving the burden of conservation on the public sector and raising concerns about long-term survival and financial instability. Furthermore, there is a critical need for emerging advanced preservation strategies, AI-assisted tools for biobanking operations, and stringent quality control procedures in biobanks meeting international standards. In addition, the findings underscore the necessity of the digitalization of culture collections to facilitate global data sharing and robust policies for access and benefit sharing of microbial resources. In conclusion, addressing these regional disparities, mitigating financial and infrastructure gaps, using emerging and advanced strategies, and digitalization of CCs are imperative for the sustainability and applicability of the microbial biobanks.
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Background/Objectives: The patency and anatomical location of the maxillary sinus ostium are critical for preventing postoperative complications in dental implant planning and sinus lift surgeries in the posterior maxilla. Narrowing or obstruction of the ostium carries risks, including the development of acute/chronic sinusitis and bone graft failure after surgery. These risks must be carefully evaluated using preoperative radiographic images. It is time-consuming for physicians to manually perform this process, and details are overlooked due to a lack of clinical experience, which can increase surgical risks. Methods: This study aims to overcome these clinical challenges and improve the reliability of radiographic evaluation. In this study, a hybrid deep learning model is proposed for the automatic detection of the maxillary sinus ostium. The proposed model combines the local feature extraction power of CNN-based models with the global context modeling capabilities of transformer-based models, creating an effective model. Additionally, the gated fusion technique efficiently combines features from various designs, significantly enhancing classification performance. Results: The proposed model was compared with six different ViT and CNN architectures established in the literature. While the highest test accuracy among pre-trained models was 89.36%, the proposed hybrid model achieved 95.03%, demonstrating strong clinical diagnostic performance. Conclusions: Based on the performance metrics obtained, we believe the proposed model can be used to determine the patency of the maxillary sinus ostium. This will lighten the workload for specialists and minimize traditional errors.
Understanding human evolution relies on biomolecular data from ancient skeletal tissues, yet warm climates often cause complete collagen loss, excluding many regions from the study. This research investigates the survival of non-collagenous proteins (NCPs) and low-molecular-weight proteins in archaeological bones deemed collagen-free by traditional metrics. Using a multi-method approach, we employed sandwich enzyme-linked immunosorbent assays for osteocalcin quantification, Qubit fluorometry for total protein, and liquid chromatography tandem mass spectrometry (LC-MS/MS) for characterization, complemented by Fourier transform infrared spectroscopy to assess the diagenetic state. Samples included collagen-depleted bones from Neolithic Lebanon and Palaeolithic France, with well-preserved controls from Neolithic Serbia and Paleolithic Russia. Results indicate that bone-associated NCPs, including osteocalcin, survive only if the insoluble collagen is preserved. Methodologically, tangential flow filtration outperformed centrifugal devices for protein recovery. EDTA demineralization with FASP was most effective for maximizing collagen recovery, while HCl demineralized protein precipitation best detected unique NCPs. Collagen was identified in the soluble supernatants of most collagen-depleted bones. Abundant collagen peptides were identified in a sample with a 0% collagen yield and a very low amide-to-phosphate ratio. These findings demonstrate that bones unsuitable for traditional dating can still retain measurable collagen, broadening the range of biomolecular analyses possible in warm and humid climates.
Phage infection undergoes a series of physiological transitions, holding crucial information about phage replication dynamics and potential phage-derived antimicrobials. Although phage-induced cytological changes have been used to infer phage-hijacking mechanisms, current approaches are limited by the lack of comprehensive single-cell morphological analysis and insufficient resolution of temporal dynamics, particularly for phages displaying short latent periods, thereby hindering systematic characterization of morphological transitions throughout the infection cycle. Here, we characterized a newly identified coliphage with a genome of 53 kbp, Tiny, which exhibits an unusually long latent period, making it an ideal candidate for resolving temporal morphological transitions. Tiny exhibits both temperature- and host-dependent killing profiles against diverse Escherichia coli strains, including ATCC 25922, uropathogenic E. coli, and avian pathogenic E. coli. While its replication efficiency is temperature-dependent, showing enhanced productivity at lower temperatures, the duration of its adsorption and latent period is largely host-dependent. Depending on the bacterial strain, Tiny exhibits either a prolonged latent period in slow-adsorbing strains or a rapid one in fast-adsorbing strains, regardless of infection temperature, suggesting phage-host compatibility. Single-cell bacterial cytological profiling of Tiny-infected ATCC 25922 cells revealed that Tiny progressively induces distinct bacterial morphological transitions throughout its lytic cycle, suggesting sequential interference with host physiology as part of its replication cycle prior to cell lysis. This work establishes a broadly applicable framework for dissecting lytic phage biology with high temporal resolution and lays the foundation for future integrative omics studies aimed at understanding how phages sequentially modulate their bacterial hosts. Antibiotics trigger unique patterns of morphological changes in bacteria, and these compound-specific signatures provide a basis for determining mechanisms of action in antibiotic discovery. By the same concept, phage-induced morphological changes can reveal key insights into phage replication dynamics and guide the identification of phage-derived antimicrobials. However, the complexity of phage biology and the variability of phage-host interactions pose challenges in interpreting these phenotypic outcomes. Here, we employed a phage-host pair that exhibits an unusually prolonged latent duration as a model to establish a broadly applicable framework for dissecting lytic phage biology with high temporal resolution. Through single-cell bacterial morphological analyses, this approach captures dynamic infection processes inducing morphological transitions across the phage replication cycle. This work provides a phenotypic analysis pipeline to advance our understanding of phage-host interactions and lays the foundation for future integrative omics studies to elucidate how phages sequentially modulate their bacterial hosts.
The domesticated apple (Malus domestica) is widely considered an admixture primarily involving M. sieversii, M. sylvestris, and M. orientalis, with possible contributions from tiny-fruited species such as M. baccata. Although this origin theory is well supported by genomic studies, the relative contributions of each progenitor species to cultivar genomes remain unclear, and taxonomic classifications of some Malus species are conflicting. The availability of new genomic tools and resources enables probing of these topics. A panel of wild Malus accessions was genotyped using the Illumina Infinium 20K apple SNP array to generate a library of species-specific SNP and haploblock alleles. After excluding heavily admixed individuals and masking admixture haplotypes in individuals with small amounts of admixture, this library was used to clarify the ancestry of 436 wild accessions and estimate the genomic proportions attributable to M. sieversii, M. sylvestris, M. orientalis, and collectively, tiny-fruited Malus species in M. domestica cultivars. The results were broadly consistent with previous studies but revealed new ancestry details and enabled precise haplotype-level attributions. Previously unreported admixture and taxonomic irregularities were detected for some wild accessions. All M. domestica cultivars were found to be mixtures of the three primary progenitors, with some also showing contributions from tiny-fruited Malus species. The relative contributions varied among cultivars, with evidence that older cultivars and traditional cultivars originating in eastern Europe or Western Asia had less M. sylvestris ancestry than modern cultivars and traditional cultivars originating in Western Europe. This study provides new insights into apple ancestry and highlights the need for clarification in Malus taxonomy. The findings have implications for germplasm management, historical research, and genetic improvement of apple cultivars. The species-specific allele library developed here offers a valuable resource for routine admixture estimation of Malus accessions genotyped on the same set of SNPs.
Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity, and early diagnosis plays a crucial role in improving patient prognosis and survival rates. Histopathological examination remains the gold standard for OSCC diagnosis; however, this process is time-consuming and highly dependent on expert interpretation. With the rapid development of digital pathology and artificial intelligence, deep learning-based approaches have emerged as promising tools to support automated diagnostic systems. In this study, five convolutional neural network (CNN) architectures-VGG16, ResNet50, InceptionV3, EfficientNetV2S, and ConvNeXt-Tiny-were comparatively evaluated for the automated classification of OSCC using histopathological images. An open-access OSCC dataset was utilized, and two experimental scenarios were created using the original dataset and an augmented dataset. The dataset was divided into training, validation, and test subsets using a stratified approach. All models were trained under identical experimental conditions using ImageNet-pretrained weights and a unified classifier head in order to ensure a fair comparison. Model performance was assessed using Accuracy, Precision, Recall, Specificity, F1-Score, and ROC-AUC metrics. Additionally, Grad-CAM was applied to visualize the image regions influencing model predictions and to enhance interpretability. The results demonstrated that data augmentation significantly improved the performance of all models. Among the evaluated architectures, ResNet50 achieved the highest diagnostic performance on the augmented dataset, reaching an accuracy of 0.91 and a ROC-AUC of 0.88, followed by EfficientNetV2S and ConvNeXt-Tiny. Visualization analyses indicated that the models focused on histopathologically relevant regions associated with tumoral structures. Overall, the findings suggest that deep learning-based approaches can effectively support patch-level automated OSCC classification from histopathological images and may contribute to future development of clinical decision support systems in digital pathology.
In this paper, we tackle a core challenge for wearable human activity recognition (HAR), namely the recognition of daily-living and locomotion activities from inertial sensor windows: delivering reliable, interpretable uncertainty on tiny microcontrollers where latency, RAM, and energy are tightly constrained. Existing embedded approaches either calibrate softmax confidences, which are cheap but brittle under sensor placement or tempo shifts, or rely on generative or posterior-sampling schemes that exceed TinyML budgets. We propose hyperdimensional distance- and uncertainty-aware human activity recognition (HDUQ-HAR), an on-device hyperdimensional computing (HDC) framework that encodes each IMU window into a bipolar hypervector, classifies via prototype similarity, and derives three complementary, lightweight uncertainty signals from the same representation: (1) distance to the class prototype, (2) similarity gap to the runner-up, and (3) vote-dispersion capturing n-gram consensus. A label-conditional conformal layer converts these scores into set-valued predictions with finite-sample coverage guarantees and exposes a human-readable reason code indicating why uncertainty increased. Across UCI HAR, WISDM, PAMAP2, and OPPORTUNITY with subject-out splits and realistic shifts (orientation, gain, time-warp, missing axis, cross-placement), HDUQ-HAR achieves near-target coverage at [Formula: see text] with near-singleton sets on i.i.d. data (average size 1.18-1.25) and robust shift/OOD detection (AUROC 0.92-0.96), while running in 3-5 ms/window on Cortex-M4 with ∼6-9 KB RAM and ∼5-7 KB Flash. By unifying HDC geometry with label-conditional conformal prediction, our method shows that efficiency and reliability can co-exist in wearables, yielding small, calibrated sets that expand gracefully under shift and actionable explanations practitioners can trust.
Existing BEV perception methods unify multi-view information in a bird's-eye-view coordinate system, yet their performance in dynamic traffic scenes remains limited by three major error sources: depth-noise amplification during image-to-BEV lifting, representation discontinuity caused by time-varying occlusion and visibility, and temporal drift induced by recursive fusion of historical BEV features. To address these issues while preserving computational tractability, we propose TriQuery-BEV, a modular enhancement framework over BEVFormer that improves BEV query modeling from the perspectives of geometric ambiguity, occlusion robustness, and temporal consistency. The proposed framework integrates three components: Query Mask (QM) for structured regularization in the BEV query space, depth-modulated hybrid positional encoding (DM-HPE) for geometry-aware positional representation, and a Temporal Query Filter (TQF) for uncertainty-aware temporal fusion. Experiments on the nuScenes benchmark demonstrate consistent improvements over BEVFormer across different model scales. TriQuery-BEV improves the nuScenes detection score (NDS) and mean average precision (mAP) by 5.4%/6.4% under the Tiny (ResNet-50) setting and by 6.0%/6.5% under the Base (ResNet-101) setting. It also reduces key true-positive error metrics, including mean translation error (mATE) by 2.9%/6.5%, mean orientation error (mAOE) by 5.7%/8.3%, and mean velocity error (mAVE) by 7.3%/15.0% for Tiny/Base, respectively. Extensive ablations further verify the effectiveness of DM-HPE, TQF, and QM, confirming improved robustness, geometric accuracy, and temporal consistency in highly dynamic environments.
Background/Objectives: The aim of this study is to test different convolutional neural network (CNN) and Transformer-based models to detect and classify vertical misfit at the abutment-prosthesis interface on panoramic radiographs, and to develop a hybrid deep learning model enhanced with attention mechanisms. Methods: A dataset consisting of a total of 566 images, manually classified as 249 'fit' and 317 'misfit' cases by two experts, was created. Images were resized to 224 × 224 and divided into training, validation, and test groups. The deep learning model yielding the most successful results was determined as the backbone; a hybrid model was developed by integrating three different attention modules (SE, CBAM, and ECA) into this structure. Model performance was evaluated using accuracy, precision, sensitivity, and F1 score metrics. Results: CNN-based models (RegNetY-800MF, ConvNeXt-Tiny, EfficientNetV2-S, ResNet50) performed better than Transformer-based models (DeiT, Swin-Tiny) in all metrics. The proposed hybrid model exhibited the highest success among all tested models with a 99.12% accuracy rate. This model reached a 100% precision value in the misfit group and yielded no false positive results. The F1 scores of the hybrid model were recorded as 99.01% for the fit group and 99.21% for the misfit group. Conclusions: The findings of this study demonstrate that attention-enhancing deep learning frameworks have the potential to significantly improve the diagnostic utility of routine panoramic radiographs. It shows that panoramic imaging, when supported by advanced artificial intelligence, can provide valuable diagnostic support in detecting vertical misfit. The developed model has the potential to become a reliable clinical decision support system.
Inhalation risk assessments that do not account for human relevant particle size distributions (PSDs) in the inhalation dosimetry adjustment can overestimate risk. This stems from a mismatch between animal toxicity studies that use small particle sizes for hazard identification and the larger PSDs characteristic of agricultural spray application exposures. This work introduces a novel approach employing Multiple-Path Particle Dosimetry (MPPD) software to derive human equivalent concentrations (HECs) that incorporate a Particle-size Adjustment Factor (PAF) in addition to the dosimetric adjustment factor (DAF), which is the traditional approach to derive the inhalation risk assessment endpoint. While the DAF accounts for the differences in rat and human respiratory physiology, the PAF accounts for differences in aerosol PSDs between occupational exposure scenarios and rodent inhalation studies. The MPPD model was used to derive the DAF and PAFs for different pesticide application methods to generate HECs for use in risk assessments. The results demonstrate that PAF integration results in refined exposure-relevant and scenario-specific risk assessments and identified scenarios with negligible potential for exceeding the inhalation exposure hazard thresholds. While more data on human-relevant PSDs for several pesticide application scenarios are needed, this method, coupled with new approach methods (NAMs) to predict portal of entry effects, and dosimetry and kinetic modeling to understand systemic dose, supports existing weight-of-evidence frameworks for reducing animal studies in pesticide registration. Advancing exposure assessment using the best available methods ensures robust human health protection while reducing animal usage. When assessing the safety of pesticide sprays that workers might breathe in, scientists traditionally compare effects in animal inhalation studies to human exposures. However, this standard approach may overestimate risk because it misses a critical difference: the sizes of particles people breathe during pesticide application are much larger than the tiny particles used in laboratory animal tests. Smaller particles penetrate deeper into lungs and cause more harm, so using animal data based on smaller particles makes real-world risks appear worse than they actually are. We developed an improved method that accounts for realistic particle sizes workers encounter when applying pesticides through different spray equipment. Using existing models, we calculated two correction factors: one that adjusts for differences between rat and human lung anatomy (the traditional approach), and a new one that adjusts for particle size differences between laboratory studies and actual human exposure. When we applied both corrections together, we found that inhalation risks were lower than previously estimated for certain scenarios. This refined approach provides more accurate, scenario-specific safety assessments and identifies some scenarios with negligible potential for exceeding safety thresholds. It also supports reducing animal testing in pesticide registration by demonstrating exposure data, combined with modern computational methods, can adequately assess safety without additional animal studies. While more measurements of particle sizes from real-world pesticide applications would strengthen this approach, our method represents an important advancement in exposure science - delivering robust safety protection while providing opportunities to reduce reliance on animal testing.
Catastrophic forgetting remains a fundamental obstacle for Artificial Neural Networks (ANNs) in continual learning. Although existing ANN-based methods can alleviate forgetting, they often introduce additional overhead and are less suitable for low-power deployment. Spiking Neural Networks (SNNs) provide an energy-efficient alternative, yet current SNN continual-learning methods still rely on external teacher models or task identifiers and lack a unified mechanism to jointly stabilize spike-level representations and synaptic parameters over long task sequences. To mitigate both limitations, this study proposes SD2-SNN, a framework employing Self-Distillation and Structural Decomposition to enhance knowledge retention without external supervision. To mitigate forgetting, SD2-SNN integrates two synergistic mechanisms: it utilizes internal self-distillation to anchor decision boundaries by aligning spike-rate distributions with prior states, and concurrently implements structural weight decomposition to decouple parameters into a stable shared base and a dynamic task-specific component. This approach effectively balances plasticity and stability while leveraging inherent SNNs sparsity. Experiments on multiple continual learning benchmarks demonstrate that SD2-SNN achieves strong and stable performance across both image-based and event-based settings. In particular, it attains 57.98% and 47.78% CIL accuracy on Split-CIFAR100 under 10-step and 20-step protocols, 71.5%/40.4% TIL/CIL on Tiny-ImageNet, and 90.3%/62.9% TIL/CIL on DVS128 Gesture.
The increasing use of UAVs has raised concerns regarding public safety and airspace security. To address air-to-air micro-UAV detection with cluttered backgrounds, tiny targets, and diverse viewing angles, this paper develops SPAE-YOLOv8, a lightweight detector based on YOLOv8n. SPAE consists of four core designs: SIoU loss, P2 shallow feature layer, ADown adaptive downsampling, and Efficient_UAVDet lightweight detection head. These modules improve small-target representation and reduce model size. In this paper, lightweight refers to the combination of parameter count, storage volume and inference speed. On the Det-Fly dataset, the proposed method achieves an mAP@0.5 of 0.922, outperforming YOLOv8n by 7.2 percentage points while reducing total parameters by 30%. We conduct independent training and testing on the DUT Anti-UAV dataset and obtain an mAP@0.5 of 0.906. Cross-dataset testing is further carried out on the more challenging Anti-UAV300 dataset without additional fine-tuning to verify the generalization performance of the model. In real-world onboard deployment, the model is implemented on an Intel NUC11TNHi7 embedded UAV platform with OpenVINO acceleration and achieves 43.9 FPS at a resolution of 640×640, satisfying real-time inference requirements. The ablation results demonstrate the contribution of the proposed modules, providing an efficient lightweight solution for airborne monitoring and civil airspace security.
To meet the demand for real-time and accurate diamond particle size measurement in industrial scenarios-where traditional image processing methods lack robustness in complex environments and existing deep learning models struggle to balance accuracy and efficiency-this paper proposes an integrated framework for dynamic segmentation and morphological analysis of diamond particles based on video streams. A fully automated data acquisition system consisting of a high-precision motion stage, an industrial camera, and an optical microscope is first constructed to capture dynamic particle images. Based on YOLOv8n-seg, a lightweight SPR-YOLOv8 instance segmentation model is then developed with three key improvements: a Large Separable Kernel Attention (LSKA) mechanism is introduced into the SPPF module to enhance feature discriminability; the RepBlock module is adopted in the neck network to improve feature fusion efficiency through structural re-parameterization; and a P2 small-object detection head is introduced while large-object detection branches are removed, enabling the model to focus on tiny, densely distributed particles. Finally, a contour-based geometric analysis method is proposed for particle size estimation based on segmentation results. Experimental results show that the proposed model achieves an mAP@0.9 of 0.861 while maintaining a low parameter count (0.97 M) and a high inference speed of 500 FPS. Compared with the conventional OpenCV-based method (CADPS), the proposed DPSCA framework reduces the mean absolute percentage error in particle size measurement by over 70%, while also demonstrating strong accuracy and stability in consecutive-frame tracking. Overall, this study provides a practical and efficient automated inspection solution for online quality control in superhard material manufacturing, and supplementary cross-scale experiments further demonstrate promising robustness on diamond particles beyond the primary 180-250 μm range.