As rail transportation plays an increasingly important role in China, the safety of rail transit operations has also attracted more and more attention. However, in some remote areas, the train track crosses the highway and pedestrian passage. In particular, pedestrians still stay on the track when the train is about to arrive, which will bring huge potential safety hazards, and accidents occur frequently. These pedestrians usually move fast and irregularly on the railway track, while the target is very small and has a high degree of coincidence of body positions within the visual range of the machine’s vision. In addition, complex and uncertain environmental factors such as trees, weeds, and telephone poles around the railway track have caused huge obstacles to pedestrian detection. It is of great significance to carry out research on pedestrian detection and abnormal state monitoring at railway stations to ensure the safety of pedestrians. Traditional machine learning target detection algorithms, such as the Viola–Jones Detector, generally use the sliding window method to extract candidate frames. They first extract and learn low and intermediate features in candidate frames, and then use classifiers to identify and select objects, which makes it difficult to solve the problems caused by fast movement, small targets, high randomness of appearance, and the high degree of coincidence of body positions. In order to better deal with these difficulties, we propose a detection algorithm based on deep learning, which can help us to obtain a better detection effect by learning the higher-level features of the object through Convolutional Neural Networks (CNNs) [1]. The deep learning target detection algorithm has been in development since R. Girshick et al. proposed Region-CNN (RCNN) [2] in 2014. Since then, Fast R-CNN [3], Faster R-CNN [4], Spatial Pyramid Pooling (SPP) [5], two-stage detectors, You Only Look Once (YOLO) [6–9], Single Shot MultiBox Detector (SSD) [10], and other single-stage detectors have emerged. The two-stage detector uses a convolutional neural network to extract the features of the markers, and then uses Region Proposal Net (RPN) to recommend candidate boxes, which returns the candidate boxes to the predicted position through a gradient descent at the end. Conversely, the single-stage detector directly performs the regression of the bounding box after extracting the features by ignoring the RPN. The two-stage detector uses two different networks to classify and locate objects, so the detection accuracy is at a high level while the speed is very slow, requiring at least 100 ms to detect an image, such as the Faster RCNN. The single-stage detector uses only one network to perform classification and positioning at the same time, so detection speed is guaranteed. The detection speed of YOLOv1 can reach 45–120 fps, which can process video or camera images in real-time, requiring less equipment and achieving better performance in field deployment. With the development of transportation, pedestrian detection has gradually become a hot spot in the field of target table detection, where many experts and scholars have put forward their views and opinions. Jin, Xianjian et al. proposed a pedestrian detection algorithm based on YOLOv5 in an autonomous driving environment [11]; Gai Y et al. proposed a method of pedestrian detection + tracking + counting based on YOLOv5 with Deepsort [12]; Sukar et al. proposed an improved YOLOv5 algorithm for real-time pedestrian detection [13]. Zhi Xu et al. proposed a method of CAP-YOLO based on channel
attention for Coal Mine Real-Time Intelligent Monitoring [14]. Masoomeh Shireen Ansarnia et al. proposed a deep learning algorithm for contextual detection in orthophotography [15]. Kamil Roszyk et al. adopted a method for low-latency multispectral pedestrian detection in autonomous driving by YOLOv4 [16]. Luying Que et al. proposed a lightweight pedestrian detection engine of a two-stage low-complexity detection network and adaptive region focusing technique [17]. Yang Liu et al. used a thermal infrared vehicle and pedestrian detection method in complex scenes [18]. Jingwei Cao et al. proposed a pedestrian detection algorithm for intelligent vehicles in complex scenarios [19]. Isamu Kamoto et al. used a deep learning method to predict crowd behavior based on LSTM [20]. Gopal, D.G. et al. proposed a method of selfish node detection based on evidence by trust authority and selfish replica allocation in DANET [21]. Jerlin, M.A. et al. created a smart parking system based on IoT [22]. Nagarajan, S.M. et al. applied an intelligent anomaly detection framework to cyber physical systems [23]. Selvaraj, A. et al. put forward a swarm intelligence approach of optimal virtual machine selection for anomaly detection [24]. Nagarajan, S.M. et al. put forward an effective task scheduling algorithm with deep learning for IoHT in sustainable smart cities [25]. The above algorithms have put forward corresponding practical innovations in pedestrian detection and processing, but few achievements have been made in railway pedestrian detection, which is one of the most high-risk scenarios. This paper aims to carry on the corresponding research and experiments for this scene. Aimed at the problem of the low detection accuracy caused by the rapid movement of the target or the prediction frame completely deviating from the target, as well as the missed detection of the target caused by the high coincidence of body positions, an improved
target detection algorithm based on YOLOv5s is proposed.

(1) L1 [26] regularization is added to constrain the scaling factor of the BN [27] layer to make the activation coefficients sparse. Next, the modified model is sparsely trained to cut out the sparse layers. We end up with a very compact model with repeated cutting.

(2) In Backbone, the CEM module is introduced to fully extract the features of different scales. The CxAM module is introduced to extract context semantic information to improve recognition accuracy. The CnAM module is introduced to correct the position of F5 layer features and improve the accuracy of target box regression.

(3) DIoU_NMS is used instead of NMS to filter prediction boxes to avoid eliminating different target prediction boxes with high consistency.
(4) We collected a certain number of datasets along with a certain number of relevant public datasets to provide data support for the verification of the actual effect of the improved model.
(5) According to the direction of improvement, a number of related ablation experiments were designed to verify the validity of each contribution.