测试科学与仪器2024，Vol.15Issue(1)：128-139,12.DOI:10.62756/jmsi.1674-8042.2024013
改进YOLOv5的无人机影像道路目标检测算法
Road target detection algorithm based on improved YOLOv5 in UAV images
摘要
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.关键词
无人机/道路目标检测/YOLOv5/损失函数/卷积注意力模块Key words
unmanned aerial vehicle(UAV)/road target detection/YOLOv5/loss function/convolutional block attention module (CBAM)引用本文复制引用
张翼,马荣贵,梁辰..改进YOLOv5的无人机影像道路目标检测算法[J].测试科学与仪器,2024,15(1):128-139,12.基金项目
This work was supported by Key Research and Development Project of China(No.2021YFB1600104) （No.2021YFB1600104）
National Natural Science Foundation of China （）
(No.52002031) （No.52002031）
Scientific Research Project of Shaanxi Provincial Department of Transportation(No.20-24K,20-25X) （No.20-24K,20-25X）
