eCBAM and saSIoU Co-Optimized YOLOv11 for Riverine Floating Garbage Classification Under Complex Aquatic Scenarios

A method for classifying floating garbage in rivers using a modified YOLOv11 algorithm is proposed to solve the problem of poor recognition of river floating objects using the conventional object detection algorithms. This approach first integrates a stronger CBAM that applies multi-scale channel attention to extract the features of floating objects of different sizes, as well as boundary-enhanced spatial attention to highlight target edge features. Second, an enhanced scenario-adapted SIoU Loss Function (saSIoU) is presented, which contains an angle-sensitive increase for large targets, shape-adaptive coefficients for irregular floating objects, and dynamic boundary blur tolerance for complex aquatic environments. Experimental validation on a self-collected dataset of river floating objects-containing six categories and 12,000 images, shows that the improved model has an mAP@0.5 of 86.48%, an mAP@0.95 of 56.44%, a precision of 80.43%, and a recall of 84.36%. Compared with the original YOLOv11, the improved model has an increase of 2.65 percentage points in mAP@0.5, and an increase of 4.27 percentage points in mAP@0.95, while remaining lightweight (2.60 M parameters, 6.44 giga floating-point operations (GFLOPs)). The proposal method has relatively better detection accuracy and real-time performance in terms of detection accuracy and real-time performance, which can provide a relatively reliable technical approach to achieve intelligent cleaning of river float garbage and water environment management.