YOLOv8 garbage occlusion detection algorithm combining dynamic convolution and attention mechanism

Due to its inadequate capacity to extract visual features, the YOLOv8 algorithm exhibits low accuracy and is not robust enough to handle the issue of several types of garbage obscuring one another. In order to improve feature extraction performance, this article modifies the YOLOv8 network structure. Initially, the traditional convolution in the backbone network is replaced by full-dimensional dynamic convolution. Second, the C2f module in the neck network is replaced with the deformable convolution structure C2F-DCNV3 module. In order to more precisely identify the trash objects that obscure one another, the CBAM attention mechanism module is finally implemented. The experimental results show that the improved algorithm has higher precision and better robustness in dealing with different occlusion types. By introducing the full-dimensional dynamic convolutional module, deformable convolutional module and CBAM attention module, the accuracy is improved by 1.6 percentage points, 2.1 percentage points and 4.3 percentage points respectively, and the feature extraction ability of the model is improved.