改进YOLOv8n的复杂环境下垃圾轻量化检测
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Improved lightweight garbage detection method for YOLOv8n in complex environments
SUN Shizhengl*，HELingling1，ZHENG Shuai²，HE Zeyin1 （1.School of Mechatronics and Vehicle Engineering， Chongqing Jiaotong University， Chongqing 400074， China; 2. School of Shipping and Naval Architecture， Chongqing Jiaotong University， Chongqing 400074， China） * Corresponding author，E-mail： sszO9101l@163.com
Abstract： To address the issues of a large number of parameters and false or missed detections by garbagedetection models in complex environments，this paper proposed a lightweight garbage-detection model based on an improved YOLOv8n. First，a lightweight network structure，MobileNet V3_ECA，was introduced as the backbone of YOLOv8n，which enhanced the model’s ability to represent garbage-feature regions and reduced the model's parameter count. Second，the Context Anchor Attention （CAA） mechanism was integrated into the backbone to strengthen the extraction of garbage-related features. Next，Omni-Dimensional Dynamic Convolution （ODConv） replaced the standard convolutions in the neck network， refining local feature mapping and enabling the fusion of fine-grained local garbage features.Finally，the Wise Intersection over Union （WIoU v3） bounding-box loss function was adopted to improve the regression performance of the network's bounding boxes. Compared with the original YOLOv8n，the improved model is improved by 1.1% in mAP @0.5 ，the detection speed is increased by 11.7% ，and the parameter Params，model size and floating-point operation FLOPs are reduced by 70.8% ， 66.1% and 70.7% ，respectively. Experimental results demonstrate that the improved model can efectively improve the detection accuracy and significantly reduce the complexity of the model，which has important engieering significance for the deployment and application of the model to the edge detection equipment.
Key words： garbage detection; lightweight; MobileNet V3_ECA; YOLOv8n; deep learning
1引言
垃圾检测与分类是实现资源循环利用、减少环境污染和保护人类健康的重要措施。（剩余19301字）
