Improved YOLOv5-based detection method for oilfield safety warning bands

With the development of deep learning technology, image-based object detection algorithms have been widely used in oilfield safety behavior regulation. However, the accuracy of identifying safety warning bands in oilfields is low, mainly due to the extreme aspect ratios. To solve the above problems, this paper proposes an improved YOLOv5-based method for detecting rotating targets of oilfield warning bands. By adding an additional angle prediction task to the original object detection framework and using a cyclic smooth labelling algorithm to transform the angle regression problem into a classification problem, the horizontal and predicted angle decoders can be combined to obtain the rotation bounding box of the target. This provides a more accurate spatial position representation of the warning band target, making it easier for the network to extract strong discriminative features of the target. Compared with traditional object detection algorithms annotated with horizontal bounding boxes, the rotation bounding box annotated object detection algorithm proposed in this paper significantly improves the recognition performance of safety warning bands and meets practical application requirements.