Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (8): 243-248.DOI: 10.3778/j.issn.1002-8331.2011-0460

• Graphics and Image Processing • Previous Articles     Next Articles

Real Time Traffic Sign Detection Algorithm Based on Improved YOLOV3

WANG Hao, LEI Yinjie, CHEN Haonan   

  1. College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
  • Online:2022-04-15 Published:2022-04-15



  1. 四川大学 电子信息学院,成都 610065

Abstract: Traffic sign detection is an important part of intelligent driving task. In order to meet the requirements of detection accuracy and real-time detection, an improved real-time traffic sign detection algorithm based on YOLOV3 is proposed. First, the cross stage local network is used as the feature extraction module to optimize the gradient information and reduce the inference computation. At the same time, the path aggregation network is used to replace the feature pyramid network, which not only solves the multi-scale feature fusion, but also preserves more accurate target spatial information and improves the targets detection accuracy. In addition, the complete intersection over union loss function is introduced to replace the mean square error loss to improve the positioning accuracy. Compared with other object detection algorithm on the CCTSDB dataset, experimental results show that, the average precision of the improved algorithm reaches 95.2% and the detection speed reaches 113.6 frame per second, which is 2.37% and 142% higher than YOLOV3 algorithm.

Key words: object detection, YOLOV3, loss function

摘要: 交通标志检测是智能驾驶任务中的重要一环。为了满足检测精度和实时检测的要求,基于YOLOV3提出一种改进的实时交通标志检测算法。采用跨阶段局部网络作为特征提取模块,优化梯度信息,减少推理计算量;同时以路径聚合网络替代特征金字塔网络,在解决多尺度特征融合的同时,保留了更加准确的目标空间信息,提高目标检测精度;并且引入完备交并比损失函数替代均方误差损失,提高定位精度。与其他目标检测算法在CCTSDB数据集上进行对比检测,实验结果表明,改进后的算法平均精度达到95.2%,检测速度达到113.6?frame/s,与YOLOV3算法相比,分别提升2.37%和142%。

关键词: 目标检测, YOLOV3, 损失函数