Machine harvesting of cotton in Xinjiang has significantly improved harvesting efficiency; however, it has also resulted in a considerable increase in residual mulch content within the cotton, which has severely affected the quality and market value of cotton textiles. Existing mulch detection algorithms based on machine vision generally suffer from complex parameterization and insufficient real-time performance. To overcome these limitations, this study proposes a novel mulch detection algorithm, Mulch-YOLO, developed on the YOLOv11 framework. Specifically, an improved CBAM (Convolutional Block Attention Module) is incorporated into the BiFPN (Bidirectional Feature Pyramid Network) to achieve more effective fusion of multi-scale mulch features. To enhance the semantic representation of mulch features, a modified Content-Aware ReAssembly of Features module, CARAFE-Mulch (Content-Aware ReAssembly of Features), is designed to reorganize feature maps, resulting in stronger feature expressiveness compared with the original representations. Furthermore, the MobileOne module is optimized by integrating the DECA Dilated Efficient Channel Attention (Dilated Efficient Channel Attention) module, thereby reducing both the parameter count and computational load while improving detection efficiency in real time. To verify the effectiveness of the proposed approach, experiments were conducted on a real-world dataset containing 20,134 images of low-visual-saliency plastic mulch. The results indicate that Mulch-YOLO achieves a lightweight architecture and high detection accuracy. Compared with YOLOv11n, the proposed method improves mAP@0.5 by 4.7% and mAP@0.5:0.95 by 3.3%, with a 24% reduction in model parameters.

Industrial surface defect detection faces challenges of complex textures, diverse defect morphologies, and scarce labeled data, especially for non-woven fabrics. This paper proposes a dual-domain reverse distillation algorithm for unsupervised defect detection (DDRD). The algorithm integrates frequency-domain wavelet enhancement and spatial-domain self-attention to enhance defect features synergistically. A Wavelet High-frequency Deformable Enhancement Module amplifies fine-grained defect details, while a Convolutional Self-Attention Spatial Global Enhancement Module captures long-range spatial dependencies. The enhanced dual-domain features are embedded into a reverse distillation framework for end-to-end training, achieving precise defect localization via feature reconstruction errors. Extensive experiments are conducted on the self-built large-scale non-woven fabric WFB dataset and the public MVTec AD benchmark dataset. The results show that DDRD achieves 98.0% pixel-level AUROC and 93.0% image-level AUROC on the WFB dataset, outperforming the state-of-the-art RD4AD method by 0.5% and 1.5%, respectively. On the MVTec AD dataset, it attains an average of 97.5% pixel-level AUROC and 99.7% image-level AUROC, with perfect 100% image-level detection accuracy on multiple categories. These results validate the efficacy and robustness of the dual-domain enhancement strategy for industrial defect detection tasks.