## 1 Introduction
[8] first applied the GAN network structure in the field of deblurring, restored blurred images through generators, differentiated clear images from blurred images by discriminators, and trained them against each other to remove the blur caused by the camera's motion. In 2019, the feature pyramid network was introduced on this basis, and the performance was improved further [9]. The research on target detection can be divided into traditional methods and methods based on deep learning. Traditional methods use appearance and color features for target detection purposes [10,11]; however, feature extraction depends on manual work and has numerous limitations, which cannot meet the real-time and accuracy requirements of obstacle detection methods when driving automatic rail locomotives. In 2014, R-CNN [12] used a convolution network for target detection for the first time. To date, deep learning-based methods have been widely used for target detection. The target detection algorithm based on the deep learning method is mainly divided into two categories: the one- and two-stage detection algorithms. One-stage detection algorithms mainly include the YOLO series [13, 14, 15]. EfficientDet [16], and SSD [17]. Two-stage detection algorithms mainly include Fast R-CNN [18] and Faster R-CNN [19]. He et al. [20] proposed a track-obstacle detection algorithm based on improved R-CNN. By introducing a new up-sampling parallel structure and context extraction module (CEM) into the architecture of R-CNN, the accuracy of obstacle detection was improved. He et al. [21] proposed a rail-transit-obstacle detection algorithm based on improved Mask R-CNN, which uses an SSwin-Le Transformer as the feature extraction network and ME-PAPN as a feature fusion network. A variety of multi-scale enhancement methods were integrated to improve the accuracy of small-target detection. He et al. [22] proposed an obstacle detection algorithm for dangerous track areas, which uses the Mask R-CNN model based on ResNet101 as the backbone feature extraction network. The experimental resultsshow that the network has a high detection accuracy for small targets. [PERSON] et al. [23] proposed a track-obstacle detection algorithm based on improved YOLOv4. D-CSPDarknet was designed as a feature extraction network, and the feature fusion network combines path aggregation and feature pyramid networks to establish a spatial pyramid aggregation network in each fusion layer to improve the detection accuracy of medium- and long-distance obstacles. [PERSON] et al. [24] proposed a flexible and efficient multi-scale single-stage target detector, FE-YOLO, for track-image-obstacle detection, and designed a repeatable bi-directional cross-scale path aggregation module as the core of the feature fusion network to improve the accuracy of track-obstacle detection. [PERSON] et al. [25] proposed a method of track-obstacle detection based on improved YOLOv3. A four-scale detection structure was formed by adding a scale to the three scales of the original YOLOv3, and the detection accuracy of small-target stones was increased. The reference [20; 21; 22; 23; 24; 25] mainly focuses on improving the detection accuracy of the algorithm but ignores the detection speed of the algorithm. For the obstacle detection of an electric locomotive driving scene, it is more necessary to achieve rapid-detection networks. [PERSON] et al. [26] proposed a lightweight network-based foreign-body-intrusion detection method for the railway regions of interest. Sparse and channel-pruning methods were used to compress the YOLOv3 model, and a lightweight railway foreign-body-intrusion detection model was constructed. This model is effective for target detection in an ideal environment, but is poor for complex environments, such as those with low illumination. [PERSON] et al.