Abstract
Aiming at the problems of low detection accuracy and inaccurate positioning accuracy of light-weight network in traffic sign recognition task, an improved light-weight traffic sign recognition algorithm based on YOLOv4-Tiny was proposed. By improving the K-means clustering algorithm, the anchor with appropriate size is generated for the traffic sign data set to improve the detection recall rate and target positioning accuracy. The strategy of large-scale feature map optimization is proposed, which enriches the feature level of the network by using the low-level information, strengthens the representation of the feature information of the small target, and improves the detection accuracy of the long-range small target. In view of the problem of missed detection of high overlapping targets in the post-processing stage of the model, the paper proposes an improved NMS algorithm to screen the prediction box, avoid deleting the prediction results of different targets, and further improve the detection accuracy and recall rate of the target. Experimental results show that, compared with the original YOLOv4-Tiny algorithm, the improved algorithm in traffic sign recognition task based on TT100K dataset, mAP and recall are improved by 5.73% and 7.29% respectively, and FPS value is maintained at about 87 f/s, which meets the accuracy and real-time requirements of traffic sign recognition task.
Highlights
The identification of traffic signs is an important research content in the field of automobile autonomous driving system and driver-assisted driving system
By introducing generalized IOU (GIoU) [24] instead of intersection over union (IOU) in the distance calculation formula in the K-means clustering algorithm, GIoU can consider the area of non-overlapping areas, that is, when the two boxes do not completely intersect, GIoU introduces non-overlapping area items, which can better reflect shape information, improving the recall rate of the algorithm model and accelerating the convergence of the model
Aiming at the existing problems of non-maximum suppression (NMS) algorithm, this paper proposes an improved algorithm based on soft-NMS [25] to complete the screening task of YOLOv4-Tiny prediction box
Summary
The identification of traffic signs is an important research content in the field of automobile autonomous driving system and driver-assisted driving system. Traffic signs contain a lot of useful information, which can prompt drivers to make a correct response to road condition information in real time, greatly reduce the occurrence of traffic accidents and improves the safety of driving [1]. The study of fast and accurate traffic sign recognition system under the real scene has important practical value and a wide range of application scenarios
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