中图分类号：TP393
文献标识码：A
DOI：10.19358/j.issn.2096-5133.2020.09.011
引用格式： 胡臣辰，陈贤富. 基于YOLO改进残差网络结构的车辆检测方法[J].信息技术与网络安全，2020，39(9)：56-60.

Vehicle detection method based on improved residual network structure of based on YOLO

Abstract：For vehicle detection task, the design of more efficient and accurate network model is a hot research. Deep network model has better feature extraction capabilities than shallow network model, but will cause the gradient to disappear and the model to overfit and other problems. Application of residual network structure can alleviate such problems. Based on the YOLO algorithm, this paper improves the residual network structure and deepens the number of network layers. A convolutional neural network model with 68 convolutional layers is designed. At the same time, the input image is preprocessed to ensure that the target is not deformed or distorted on the image. Finally, the model is trained and tested on a custom vehicle data set, and the experimental results are compared with the YOLOV3 model. The experiment shows that the model detection accuracy(AP) designed in this paper reaches 90.63%, which is 4.6% higher than YOLOV3.

Key words :object detection；YOLO；residual network；deep learning