Aiming at the problem of low detection accuracy of traditional traffic sign recognition algorithms,a traffic sign recogni-tion method based on cache-convolutional network(Cache-DCN)YOLOX is proposed.In this method,the deformable convolutional network(DCN)is used to replace the ordinary convolution in the backbone,which effectively increases the receptive field of the mod-el and improves the ability of feature extraction;The EIoU loss function is used instead of the GIoU loss function in YOLOX,which optimizes the training model and improves the convergence speed;The training processes of the strength and weak stages in the YOLOX algorithm are optimized and designed to enhance the generalization performance of the model.At the same time,the cache scheme is added to further improve the detection accuracy.Experiments are conducted on the traffic sign dataset TT100K,the detec-tion accuracy of the proposed method reaches by 67.2％,it is 6.4％higher than that of the original YOLOX algorithm,and the pro-posed method can accurately detect traffic signs with good confidence and meet practical needs in various disturbed environments such as occluded small targets.
关键词
深度学习/YOLOX/交通标志识别/可变形卷积/小目标检测
Key words
deep learning/YOLOX/traffic signs recognition/deformable convolution/small target detection
维普
万方数据