Road Obstacle Detection Method Based on Improved YOLOv5

Road obstacle detection is essential for ensuring the smooth operation of roads and safeguarding the lives and property of travelers. However, current obstacle detection methods face challenges such as missed detections and false positives. To address these issues, an enhanced obstacle detection algorithm based on YOLOv5 (YOLOv5-EC3F) is proposed. First, an effective multi-scale feature fusion module (EMFF) is introduced to extract multi-scale features from the input feature map, providing richer semantic information and enhancing the perceptual range. Second, the SPPF module is replaced with the C3SPPF module to improve the model’s understanding of contextual information and increase its multi-scale adaptability. Experimental results demonstrate that, on the custom dataset, YOLOv5-EC3F raises the mAP by 3 percentage points to 82% and the recall by 7 percentage points to 78%, without compromising precision. This study offers a valuable optimization strategy for the practical application of road obstacle detection.