Research on Cone Bucket Detection Algorithm Based on Improved YOLOv5s

In order to address the problems associated with low detection accuracy, weak detection ability of small targets, insufficiently obvious differentiation of colors, and inability to accurately locate the actual position of the target object in the Formula Student Autonomous China, the YOLOv5s algorithm is improved by adding coordinate attention, modifying the color space transformation module, and adding a normalized Gaussian Wasserstein distance module and a monocular camera distance measurement module. Finally, it is experimentally verified that by adding and modifying the above modules, the YOLOv5s algorithm’s precision is improved by 6.9%, recall by 4.4%, and mean average precision by 4.9%; although the detection frame rate decreases, it still meets the requirement. Monocular camera distance measurement has a maximum error of 5.64% within 20 m in the Z-direction and 5.33% in the X-direction.