国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Aiming at the problem of low detection accuracy of traditional traffic sign recognition algorithms, a traffic sign recognition method based on Cache-DCN YOLOX is proposed. In this method,deformable convolution is used to replace the ordinary convolution in the backbone, which effectively increases the Receptive field of the model and improves the ability of feature extraction;EIOU loss function is used instead of GIOU loss function in YOLOX to optimize the training model and improve the convergence speed;The training process of the strength stage in the YOLOX algorithm was optimized and designed to enhance the generalization performance of the model. At the same time, a cache scheme was added to further improve the detection accuracy. Experiments were conducted on the traffic sign dataset TT100K, and the proposed method achieved a detection accuracy of 67.2%, which is 6.4% higher than the original YOLOX algorithm. At the same time, in various disturbed environments such as occluded small targets, the proposed method can accurately detect traffic signs with good confidence and meet practical needs.