基于改进YOLOV4的铁轨裂缝目标检测算法

针对铁轨表面裂缝的小目标特征及传统检测方法精度低，速度慢等问题，提出一种基于改进YOLOV4的目标检测算法。首先，使用改进的RFB(receptive field block)模块替换空间金字塔池化(spatial pyramid pooling, SPP)结构，以获取特征图更大的有效感受野区域，提升算法的检测精度；其次，采用深度可分离卷积结构替代网络模型中的普通卷积结构，使网络轻量化、提升检测速度；同时，利用K-means++算法重新获取锚框，再对得到的锚框进行线性尺度变化，解决原锚框不适合小目标检测的问题。结果表明改进的YOLOV4算法，平均精度均值(mean average precision,mAP)达到84.8%,相对于原YOLOV4算法提高了3.4%;检测速度(frames per second,FPS)为62.39帧/s,提高了4.07帧/s。

Research on rail crack detection algorithm based on improved YOLOV4

Aiming at the small target characteristic of rail surface crack and the low precision and slow speed of traditional detection methods,we propose an object detection method based on improved YOLOV4 network for cracks on the surface of rails in this paper.Firstly,in order to obtain the larger effective receptive field area of the feature map and improve the detection accuracy,we use the improved receptive field block (RFB) module to replace the spatial pyramid pooling (SPP) structure;Secondly,we use the deep separable convolution structure to replace the common convolution structure in the network model,so that the network is lightweight and the detection speed is improved;At the same time,we use K-means + + algorithm to reacquire the anchor frame, and then change the linear scale of the anchor frame to solve the problem that the original anchor frames are not suitable for small target detection.The results show that the mean average precision (mAP) of the improved YOLOV4 is 84.8%,which is 3.4% higher than that of the original YOLOV4 algorithm;The detection speed (FPS) is 62.39 frame/s,which increases by 4.07 frame/s.