基于Cache-DCN YOLOX算法的交通标志检测方法研究

Research on Traffic Sign Detection Method Based on Cache-DCN YOLOX Algorithm

针对传统方式识别交通标志算法存在的检测精度较低的问题,提出了一种基于Cache-DCN YOLOX算法的交通标志识别方法;在该方法中,使用DCN可变形卷积替换backbone中的普通卷积,有效地增大了模型的感受野,提高了特征提取能力;使用EIoU损失函数代替YOLOX中的GIoU损失函数,优化了训练模型,提高了收敛的速度;优化设计了YOLOX算法中的强弱两阶段的训练过程,增强了模型的泛化性能,同时加入cache方案,进一步提高了检测精度;在交通标志数据集TT100K上进行了实验,提出方法的检测精度为67.2%,比原YOLOX算法的检测精度提升了6.4%,同时,在被遮挡的小目标等多种受干扰的环境下,提出的方法能够精确地检测出交通标志,并有着较好的置信度,满足实际需求。

Aiming at the problem of low detection accuracy of traditional traffic sign recognition algorithms,a traffic sign recognition 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 model 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 detection accuracy of the proposed method reaches by 67.2%,it is 6.4%higher than that of the original YOLOX algorithm,and the proposed method can accurately detect traffic signs with good confidence and meet practical needs in various disturbed environments such as occluded small targets.