关键词: YOLOv3模型, Tiny-YOLOv3模型, 目标检测, 模型压缩, 交通标志

Abstract:

YOLOv3 target detection algorithm has high detection accuracy and fast detection speed, which can realize the real-time detection of traffic signs. However, because YOLOv3 model requires the equipment to have strong computing power and large memory, it is difficult to be directly deployed on the platform with limited resources such as vehicles. To solve this problem, a Strong Tiny-YOLOv3 target detection model is proposed. By introducing FireModule layer for channel transformation, the Strong Tiny-YOLOv3 can not only reduce the model parameters, but also deepen the model depth. At the same time, the model adds short-cut between FireModule layers to enhance the ability of network feature extraction. The experimental results show that the module can greatly reduce the dependence of YOLOv3 model on equipment while maintaining high detection accuracy. Compared with the Tiny-YOLOv3 model, the parameters of Strong Tiny-YOLOv3 model is reduced by 87.3%, the actual memory size is reduced by 77.9%, the detection speed on GeForce 940MX is increased by 22.8%, and the detection mAP on GTSDB and CCTSDB traffic sign data sets is increased by 12% and 3.8%, respectively.

Key words: YOLOv3 model, Tiny-YOLOv3 model, target detection, module compression, traffic sign