Road target detection algorithm based on improved YOLOv5 in UAV images

Abstract

Aiming at the problems such as low accuracy and poor robustness of target detection caused by missed detection of small road targets and occlusion between targets in UAV images, an improved road target detection algorithm based on YOLOv5 combining convolutional block attention module(CBAM), called YOLOv5s-FCC, was proposed. Firstly, a small target sensing layer was introduced to improve the multi-scale model, and a small target YOLO detection head was added to improve the feature extraction ability of the network for small road targets. Secondly, the CBAM fused space and channel information to enhance important information in the network after it was introduced into different locations of the Backbone network to obtain the best fusion location of CBAM. Finally, CIoU loss function was used to improve the speed and accuracy of the calculation required for predicting the bounding box of image. The experimental results showed that compared with YOLOv5 algorithm, YOLOV5-FCC algorithm can improve mAP50 and mAP50-95 by 2.0% and 4.2%, respectively. The effectiveness of YOLOv5-FCC algorithm was also verified on VisDrone dataset, and the results showed that the established system can realize automatic detection of road targets.

Keywords

unmanned aerial vehicle(UAV) / road target detection / YOLOv5 / loss function / convolutional block attention module(CBAM)

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