## 1 Introduction
Moreover, non-contact detection methods are unable to determine the precise location, shape, and size of foreign objects, resulting in a limited scope of detection information. With the rapid advancement of neural networks and deep learning technologies in recent years, railway professionals have integrated these with image and video analysis to detect and alarm against foreign object intrusions in railway slope scenarios, conduct hazard assessments, and perform real-time monitoring of the operational environment. Unlike traditional non-contact video detection methods, neural networks not only enhance the learning capability of the entire detection system but also offer faster detection speeds and stronger adaptability, significantly improving the efficiency of railway operation and maintenance safety management. Currently, target detection algorithms based on convolutional neural networks (CNNs) have been widely applied in railway foreign object intrusion detection. As an image processing technique enabled by deep learning algorithms, target detection technology can identify and locate specific targets within images, constituting a crucial technology in the field of computer vision. Notable architectures include Faster R-CNN [10], YOLO [11], and SSD [12], among which the YOLO algorithm is particularly noted for its speed, accuracy, and end-to-end characteristics. Over the past few years, the field of target detection has seen rapid advancements. [PERSON] et al. [13] have utilized convolutional neural networks for image recognition in applications such as vehicle navigation, obstacle avoidance, and decision-making. [PERSON] et al. [14] have combined machine vision facial detection technology with deep neural networks to achieve high-precision facial recognition under various environments and conditions. [PERSON] et al. [15] have employed multi-camera setups and a modified lightweight YOLOv3 architecture for detecting and tracking drones, demonstrating the application of deep learning algorithms in real-time object detection and classification within the realm of machine vision. Today, railway slope foreign object intrusion detection represents one of the most widely applied scenarios in railway operation and maintenance safety management [16]. Neural network-based target detection methods for railway slope foreign object intrusion can enhance detection accuracy, reduce the frequency of false negatives and positives, accelerate inference speed, and facilitate deployment. Neural network-based detection methodologies are highly reliant on the structure of the model itself and the datasets used for training. In specific target scenarios, prevalent mainstream models face challenges with limited generalization and low detection accuracy. Due to the complexity of slope environments, conventional neural networks often struggle to distinguish the mobility of rockfalls on the target slope and frequently misidentify high-speed moving rockfalls and stationary rock blocks as similar threats. Given that these systems can only recognize targets, such singular detection results fail to provide sufficient and effective information for comprehensive early warning systems. Furthermore, the lack of open-source rockfall datasets hampers effective model training, significantly diminishing the practical effectiveness of detections. To address these challenges, this paper utilizes imagery data from the target construction section to develop a proprietary dataset for existing railway line slope scenarios. Concurrently, by conducting a comparative analysis of mainstream activation functions within the YOLOv5 model framework, function configurations suitable for specific scenarios have been identified, and issues related to model overfitting have been addressed.