Detecting container box numbers based on improved YOLOv5 network

Efficient container transportation management relies heavily on accurate and timely detection of container box numbers. Recognizing the limitations of current manual recording and traditional detection methods, we present an enhanced YOLOv5-based algorithm. This algorithm incorporates a spatial channel attention mechanism to bolster target feature extraction and undergoes iterative training for refinement. Experimental results showcase a remarkable average detection accuracy of 98.5% and a swift detection frame rate of 54 FPS. In comparison to conventional methods, our approach significantly improves detection performance, enabling real-time capabilities suitable for diverse operational environments in container transportation management.