# An Improved Target Network Model for Rail Surface Defect Detection  
[PERSON]  
1 Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China 12 Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China 2* School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 2* State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China 2* Correspondence: [EMAIL_ADDRESS] (Y.Z. ): [EMAIL_ADDRESS] (G.C.) [PERSON]  
1 Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China 12 Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China 2* School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 2* State  
to exert a substantial impact on the operational safety of railway transportation. Failure to swiftly and diligently detect these imperfections could jeopardize the safety of train operations and the life and property of passengers. Consequently, to maintain the optimal operational condition of the rails, it is imperative to conduct periodic examinations of the rail system. The expeditious and intelligent detection of surface imperfections on rails has emerged as an exceptionally vital aspect of railway transportation. There is a growing realization of the necessity for advanced inspection and monitoring technologies to ensure the integrity and reliability of railway tracks. As a result, non-destructive testing methods such as ultrasonic detection, eddy current detection, laser detection, and acoustic emission have been proposed. These methods belong to the domain of physical detection techniques, rooted in the principles of physics with hardware as their core. They facilitate physical phenomena induced by acoustic, optical, electromagnetic, and similar fields to evaluate the quality of rails through the detection of physical manifestations on the rails [11]. Ultrasonic and eddy current detection methods are commonly favored for their noncontact characteristics among the various options available. The ultrasonic detection method harnesses the unique properties of sound waves to identify internal defects and rail fractures. While excelling at pinpointing tiny cracks with high precision, it may encounter limitations in detecting medium to large cracks [12]. Electromagnetic acoustic emission (EMAE) technology can effectively avoid the interference of wheel-rail rolling noise (WRRN) and displays an excellent performance in the field of rail crack detection, but the detection of small cracks is still limited at high train speeds [13]. Eddy current testing, utilizing eddy current technique (ECT) probes and post-processing systems, can detect railroad cracks [9; 14]. However, this method faces challenges with respect to identifying rail faults at different levels of severity. In terms of railway systems, laser detection is employed for two-dimensional contour measurements, thereby facilitating a detailed assessment of the conditions prevalent on the railway track surface [15]. Some scholars have collected track inspection images and ultrasonic B-scan images by using the camera and ultrasonic equipment of the rail inspection vehicle, used algorithms to segment the track surface images captured by the camera and filter the ultrasonic operations, and finally tested the images using the trained model [16]. In recent years, artificial intelligence (AI) has led to the widespread application of detection technologies in various fields, such as 3D modeling from unmanned aerial vehicle (UAV) point clouds [17]. This is particularly evident in the automatic identification of defects in civil infrastructure, such as automatic pavement texture recognition, tunnel crack detection, rail track identification, and unwanted object detection [18; 19; 20].