YOLOv3-A: a traffic sign detection network based on attention mechanism - Fan GUO; Yongxiang ZHANG; Jin TANG; Weiqing LI (Tongxin xuebao)
To solve the problem that the existing YOLOv3 algorithm had more false detections and missed detections for traffic sign detection task with small target problems and complex background, based on the YOLOv3, a channel attention method for target detection and a spatial attention method based on semantic segmentation guidance were proposed to form the YOLOv3-A (attention) algorithm.The detection features in the channel and spatial dimensions were recalibrated, allowing the network to focus and enhance the effective features, and suppress interference features, which greatly improved the detection performance.Experiments on the TT100K traffic sign data set show that the algorithm improves the detection performance of small targets, and the accuracy and recall rate of the YOLOv3 are improved by 1.9% and 2.8% respectively.