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<p>To make sorting more efficient, promote the recycling of resources, and lower the costs of environmental management, it is important to be able to accurately identify waste categories. However, current waste detection methods encounter substantial obstacles, such as inadequate feature extraction for diminutive waste objects, disproportionate contextual information application in intricate backgrounds, and a notable decline in classification accuracy in occlusion situations. To address these limitations, this study introduces an enhanced waste detection model derived from YOLOv11n, termed YOLO4SWD (YOLO for Smart Waste Detection), which integrates three principal innovations. (1)The CoordA module is embedded into the backbone to accurately capture fine local features of tiny waste and boost feature discrimination between waste and low-contrast backgrounds. (2) A lightweight BasicRFB block is integrated into the neck layer to enlarge the model’s effective receptive field.(3) The FSEAM module is adopted in the detection head to preserve the effective features of unoccluded regions and block interference from occluded regions, thereby improving the accuracy of waste detection in scenarios where occlusions are common. The experimental results show that the proposed YOLO4SWD model is much better than the baseline YOLOv11n. The proposed model exhibits robust detection performance, achieving 81.6%, 69.9%, and 62.5% in mAP@0.5, mAP@0.75, and mAP@0.5:0.95, respectively. This indicates that it performs better in detecting trash in complex environments.</p>

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YOLO4SWD: an improved YOLOv11-based method for waste detection and classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月18日

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  • Xuehao Chen,
  • Fafu Zhang,
  • Wenlin Pan,
  • Yanfang Zhao

摘要

To make sorting more efficient, promote the recycling of resources, and lower the costs of environmental management, it is important to be able to accurately identify waste categories. However, current waste detection methods encounter substantial obstacles, such as inadequate feature extraction for diminutive waste objects, disproportionate contextual information application in intricate backgrounds, and a notable decline in classification accuracy in occlusion situations. To address these limitations, this study introduces an enhanced waste detection model derived from YOLOv11n, termed YOLO4SWD (YOLO for Smart Waste Detection), which integrates three principal innovations. (1)The CoordA module is embedded into the backbone to accurately capture fine local features of tiny waste and boost feature discrimination between waste and low-contrast backgrounds. (2) A lightweight BasicRFB block is integrated into the neck layer to enlarge the model’s effective receptive field.(3) The FSEAM module is adopted in the detection head to preserve the effective features of unoccluded regions and block interference from occluded regions, thereby improving the accuracy of waste detection in scenarios where occlusions are common. The experimental results show that the proposed YOLO4SWD model is much better than the baseline YOLOv11n. The proposed model exhibits robust detection performance, achieving 81.6%, 69.9%, and 62.5% in mAP@0.5, mAP@0.75, and mAP@0.5:0.95, respectively. This indicates that it performs better in detecting trash in complex environments.