## 1 Introduction  
With the continuous increase in railway transportation speed and load, steel rails are highly susceptible to defects such as cracks, spalling, and corrosion due to prolonged and repetitive exposure to train movements [1]. In high-intensity applications like high-speed rail and heavy-load railways, track surface defects not only affect the service life of railway equipment but also directly pose a threat to train operational safety. Due to the immense pressure exerted by trains on the rails, coupled with the effects of weather, temperature variations, and track wear, these defects can gradually worsen if not promptly detected and repaired, potentially leading to catastrophic railway safety incidents, including train derailments and track fractures [2]. As a result, efficient and accurate detection and diagnosis of track defects has become a critical research topic for railway maintenance and safety assurance. With the ongoing expansion of railway networks and increasing safety requirements, the development of technologies capable of providing efficient, real-time, and precise detection has become an urgent priority. Traditional track defect detection methods primarily rely on manual visual inspection or large specialized detection equipment, such as ultrasonic, eddy current, and flux leakage-based non-destructive testing (NDT) techniques [3]. While these methods have improved detection accuracy and efficiency to some extent, they still face several challenges. Firstly, manual inspection depends on the experience and intuition of maintenance personnel and is influenced by fatigue and subjective factors, making detection accuracy unreliable. This issue is especially pronounced in large-scale track inspections, where human inspection is inefficient and difficult to sustain over long periods. Secondly, although non-destructive testing equipment, including large detection vehicles, can provide relatively accurate results, they are costly; time-consuming; and cannot meet the long-term, continuous, and real-time monitoring needs of the railway. Furthermore, traditional detection methods often suffer from the risks of missed or false detections in complex track environments, particularly in scenarios with narrow cracks, small defects, and intricate surface textures. This issue is particularly prominent in high-speed rail environments [4]. For example, small defects like cracks or corrosion may be difficult to identify using traditional detection technologies, thereby compromising train safety. Consequently, the use of emerging technologies such as computer vision, deep learning, machine learning, and wireless sensor networks for high-frequency, real-time, and precise detection of track surface defects has become a focal point for both academia and industry. In recent years, deep learning has achieved remarkable results in computer vision tasks such as object detection, image classification, and semantic segmentation, offering new approaches to solve issues such as the diversity, complexity, and varying target sizes encountered in track surface defect detection. In the field of track inspection, researchers have proposed methods for detecting track cracks, spalling, and fatigue defects based on deep convolutional neural networks [4]. Compared to traditional handcrafted feature-based detection algorithms, deep networks demonstrate superior robustness and generalization capabilities when dealing with complex lighting, background interference, and large-scale data [5;6]. Moreover, with the rise of mobile and edge computing, lightweight deep models designed for track surface defect detection have emerged, capable of performing real-time inference while maintaining detection accuracy.