改进YOLOv12的水面垃圾检测方法OA

To address the issue of outdated models,high rates of missed and false detections,and the inability of existing water surface debris detection methods to meet the requirements of efficient water surface environmental management,this paper proposes an improved water debris detection method,WDW-YOLO,based on YOLOv12.This method intro-duces WTConv based on wavelet transform to improve the C3k2 module in the neck network,designing the C3k2_WTConv module.This design effectively expands the model's receptive field while maintaining controllable model parameters.For the C3k2 module in the backbone network,it introduces DynamicConv to innovate and obtain the C3k2_DynamicConv module.This approach increases the number of learnable parameters while limiting the growth of computational load,thereby enhancing the model's ability to capture feature information and improving detection accuracy.Additionally,it replaces CIoU with WiseIoUv3 and explore the optimal combination of hyperparameters to balance samples with different detection difficulties and enhance the model's ability to detect small objects.Experiments are conducted on the FloW_IMG dataset,including comparative and ablation studies.The results show that the proposed WDW-YOLO model achieves a 2.7 and 1.8 percentage points increase in mAP50 and mAP50-95,respectively,compared to the baseline YOLOv12n model,reaching 90.7%and 50.6%.This improvement is achieved while reducing model complexity and enhancing detec-tion accuracy.The WDW-YOLO model demonstrates high reliability and practicality for automated water surface cleaning and water body protection.