Train assisted driving depends on the real-time detection of train operating environment. There are abundant small targets in the images of train operating environment. Compared with large and medium targets， small targets with the proportion of less than 1% of original image have problems of high missed detection and poor detection accuracy due to low resolution. Therefore， a target detection algorithm based on improved YOLOv3 in train operating environment was proposed， namely YOLOv3-TOEI （YOLOv3-Train Operating Environment Image）. Firstly， k-means clustering algorithm was used to optimize the anchor to speed up the convergence of the network. Then， dilated convolution was embedded in DarkNet-53 to expand the receptive field， and Dense convolutional Network （DenseNet） was introduced to obtain richer low-level details of the image. Finally， the unidirectional feature fusion structure of original YOLOv3 was improved to bidirectional and adaptive feature fusion structure， which realized the effective combination of deep and shallow features and improved the detection effect of the network on multi-scale targets （especially small targets）. Experimental results show that compared with original YOLOv3 algorithm， YOLOv3-TOEI algorithm has the mean Average Precision （mAP）@0.5 reached 84.5%， which increased by 12.2%， and the Frames Per Second （FPS） of 83， verifying that this algorithm has better detection ability of small targets in images of train operating environment.