Sorting defective cocoons is a critical procedure in the silk reeling industry to ensure the quality of raw silk products. Currently, this process relies heavily on manual inspection, which is labor-intensive, subjective, and inefficient. While automated sorting based on machine vision offers a promising alternative, existing object detection algorithms struggle to balance accuracy and computational complexity, particularly when detecting tiny surface defects or distinguishing morphologically similar cocoons in dense scenarios. To address these challenges, this paper proposes an efficient silkworm cocoon defect detection model named LMD-YOLO, based on the YOLOv10 architecture. In this model, we introduce three key improvements to enhance feature extraction and multi-scale perception. First, we integrate a Large Separable Kernel Attention (LSKA) module into the C2f structure (C2f-LSKA) of the backbone. This design decomposes large kernels to capture global shape features with minimal computational cost, effectively distinguishing double cocoons from normal ones. Second, we replace standard upsampling with a DySample module in the neck, which utilizes dynamic point sampling to recover fine-grained texture details of tiny defects like surface stains. Third, a Multi-Scale Dilated Attention (MSDA) mechanism is embedded before the detection heads to aggregate semantic information across different scales, improving robustness against background interference. YOLOv10 was selected as the baseline due to its NMS-free characteristic, which mitigates the latency caused by post-processing in high-speed sorting tasks. Evaluations on a self-constructed multi-category dataset indicate that LMD-YOLO surpasses established detectors, including YOLOv8n and Faster R-CNN. Relative to the YOLOv10n baseline, our method improves mAP@0.5 by 3.11%, achieving 94.46%. Notably, Precision and Recall are increased by 3.50% and 2.97%, reaching 89.98% and 93.61%, respectively. With a compact size of 2.68 M parameters and an inference speed of 115 FPS, the proposed model offers a practical trade-off between accuracy and latency for real-time cocoon defect detection.

Feature selection is an essential technique for high-dimensional data analysis, aiming to identify informative features while eliminating redundant and irrelevant ones to improve learning efficiency and predictive performance. However, existing unsupervised feature selection methods still face challenges in effectively capturing nonlinear dependencies among features and exploiting intrinsic feature correlations without label information. To address these limitations, this paper proposes a Self-Representation Sparse Framework via Kernelized Feature Relationship Learning (SSFSK) for unsupervised feature selection. The proposed framework integrates kernelized feature relationship modeling, self-representation learning, and sparse optimization into a unified objective function. Specifically, a Gaussian kernel function is employed to transform the original feature space into a nonlinear kernel space, enabling the characterization of complex relationships among features. Based on the learned kernel representation, a self-representation mechanism is introduced to explore feature dependencies, while an $\ell_1$-norm regularization constraint is adopted to enforce sparsity and automatically identify informative feature subsets. An efficient iterative optimization algorithm is developed to solve the proposed objective function. Extensive experiments conducted on ten benchmark datasets from the UCI repository demonstrate that SSFSK consistently achieves superior classification performance and higher stability compared with several state-of-the-art unsupervised feature selection methods. Furthermore, comprehensive evaluations on low-, medium-, and high-dimensional datasets verify the robustness and scalability of the proposed method. The results confirm that combining nonlinear feature relationship learning with sparse self-representation provides an effective strategy for unsupervised feature selection in high-dimensional data analysis.

Monitoring forest diseases is essential for sustainable forestry management and ecological security. However, unmanned aerial vehicle (UAV)-based forest inspection remains challenging because diseased pine canopies are often small, densely distributed, partially occluded, and easily confused with complex background patterns caused by illumination variations, shadows, and surrounding healthy vegetation. To address these challenges, this paper proposes a lightweight detector, termed Lightweight Feature Transfer YOLO (LFT-YOLO), based on the YOLO26n architecture. The central design of LFT-YOLO is a lightweight small-target information preservation–reconstruction pathway. First, a Local Feature Injection (LFI) module is introduced to establish an independent shallow-feature transfer path, enabling high-resolution spatial and texture information to be directly aligned with and injected into the P3 feature space, thereby alleviating the loss of fine-grained information for small targets during progressive downsampling and multi-scale fusion. Second, conventional fixed upsampling in the neck is replaced with DySample-based adaptive upsampling to improve spatial reconstruction and cross-scale feature alignment. Experiments on the public Pests and Diseases Tree-Low-Level (PDT-LL) dataset show that LFT-YOLO achieves a Precision of 87.75%, a Recall of 87.02%, and an mAP@0.5 of 93.31%, corresponding to a 0.51-percentage-point improvement over the YOLO26n baseline. More importantly, the size-specific evaluation demonstrates that the performance gains are concentrated on small targets: compared with YOLO26n, small-object AP@0.5, AP@0.75, AP@0.5:0.95, Precision, and Recall improve by 2.15, 2.55, 2.15, 2.10, and 2.30 percentage points, respectively. These results indicate that the proposed architecture provides more pronounced benefits for the dominant small-object detection regime while largely preserving the performance of medium- and large-scale targets. LFT-YOLO contains only 2.43 M parameters and requires 5.80 GFLOPs. On an NVIDIA Jetson Orin Nano Super 8 GB platform using TensorRT FP16 under a fixed 15 W power mode, the model achieves 90.39 ± 0.40 FPS, with a P95 inference latency of 11.37 ms and an average power consumption of approximately 8.53 W. These results demonstrate that LFT-YOLO provides a favorable accuracy–efficiency trade-off and is suitable for real-time UAV-based detection of small and densely distributed pine wilt disease targets under resource-constrained edge-computing conditions.