Computer and Modernization ›› 2024, Vol. 0 ›› Issue (10): 27-34.doi: 10.3969/j.issn.1006-2475.2024.10.005

Previous Articles     Next Articles

Improved Roadside Monocular View Small Target Detection Algorithm Based on YOLOv5

  

  1. (1. School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; 2. Unicom Internet of Things, LLC, Nanjing 210003, China)
  • Online:2024-10-29 Published:2024-10-30

Abstract: Aiming at the problems of low recognition accuracy and fewer features of long-distance targets and small targets in the roadside view under vehicle-road cooperative sensing traffic scenarios, an improved algorithm for small target detection based on YOLOv5 is proposed. Firstly, in the backbone network, the GAM attention module is added to enhance the feature extraction ability of the network. Secondly, RepBi-PAN is introduced to replace the PANet structure of the original neck network to increase the network’s ability to localize small targets. Finally, the use of SIoU loss function instead of the original CIoU loss function can effectively avoid the arbitrary matching of the prediction frames in the regression process, thus enhancing the robustness of the model and accelerating the training speed of the network model. The experimental results show that compared with the original YOLOv5 6.0 version, the average accuracy mAP of each category is improved by 6.9 percentage points when the intersection over union IoU is 0.5, and the average accuracy mAP of each category is improved by 6.4 percentage points when the intersection over union IoU is 0.95, which effectively improves the detection capability of small target detection in the road-side view.

Key words:  , vehicle-circuit collaboration perception; roadside view; attention mechanism; small target detection

CLC Number: