Research on lightweight waste-surface detection algorithm based on improved YOLOv5s

In the context of the embedded system for water-surface garbage collection devices, the existing model is insufficiently lightweight, resulting in suboptimal real-time detection of water-surface garbage. This study introduces a V5-MBCE algorithm specifically designed for water-surface garbage recognition. By substituting the YOLOv5s backbone network with the MobileNetV3 network, the model achieves a lightweight improvement. Moreover, the activation function within the MobileNetV3 network is modified to GELU, thereby enhancing the robustness of model training. To augment the feature fusion capacity of water-surface garbage at various scales, the feature fusion network is altered to incorporate a BiFPN structure. Additionally, the inclusion of the CBAM attention mechanism bolsters the model’s focus on detecting targets. The loss function has been refined to EIoU, allowing for more precise border positioning of the prediction box. Experimental results demonstrate that the proposed algorithm attains a detection accuracy of 94.4%,representing a 1.2 percentage point increase compared to the original model. Furthermore, the model size is reduced to 7.1MB, approximately 54% smaller than the original model. Finally, combining the v5-MEBC model and binocular ranging algorithm, the detection and collection of water-surface garbage can be realized in the collection device.