# An Improved Target Network Model for Rail Surface Defect Detection
In comparison with traditional manual inspection methods, AI models indicate superior computational performance, faster detection speeds, and a more cost-effective and secure approach. Therefore, the utilization of AI deep learning models for the automated identification and assessment of defects and service conditions in railway tracks is feasible. The effective combination of digital twins (DTs) and sixth-generation (6G) vehicle-to-electronic (V2X) communications can enhance the analysis of driver behavior and enable fast and accurate diagnosis of vehicle operating conditions to help in vehicle decision-making tasks [21]. [PERSON] et al. proposed a transformer-based hybrid model that can simultaneously utilize temporal and spatial features to identify anomalies in railway heating, ventilating, and air conditioning (HVAC) systems, achieving good performance [22]. [PERSON] et al. proposed a model that can simultaneously utilize temporal and spatial features to identify anomalies in railway HVAC systems, achieving good performance [23]. [PERSON] et al. proposed a method employing the probability structure topic model (STM) for the classification and defect detection of automatic fasteners in railway inspection systems [24]. The SeMA-UNet model optimizes deep learning models in data-constrained scenarios, conducts comprehensive feature extraction on railway images, and can achieve better results than traditional models [25]. [PERSON] and colleagues devised an automated object detection system capable of discerning obstacles on the railway tracks, including curved sections [26]. [PERSON] et al. proposed the PPNN network by combining probability neural network and a particle swarm optimization algorithm; they achieved satisfactory results in recognizing subway vehicle noise using this network [27]. [PERSON] et al. devised a fusion model that harnesses deep convolutional neural network (CNN) features in combination with a support vector machine (SVM) for the classification of rail defects [28]. [PERSON] et al. conducted an analysis of a fusion methodology for images acquired by a high-speed 3D laser rangefinder, predicated on pixel and histogram similarities. This approach entails reduced computational complexity, rendering it applicable for real-time surveillance [29]. In order to overcome the problem of requiring a large number of training samples for most CNN models, some researchers have used ensemble models for detecting rail surface defects. The detection results indicate that the ensemble algorithm outperforms single detection architectures [30]. [PERSON] et al. proposed a method based on an improved YOLOv5s, which utilizes techniques such as data augmentation, introducing a global attention mechanism and optimizing loss functions to enhance the detection accuracy of rail surface defects [31]. [PERSON] et al. applied the RAG-PaDiM algorithm to railway track defect segmentation. By utilizing a residual attention-guided U-Net algorithm to generate embedding vectors and by performing operations such as residual connections and attention gates to provide regional weights, the algorithm achieves accurate pixel-level area curve segmentation and improves task performance [32]. [PERSON] et al. employed a depth data-driven model and transfer learning approach. They adapted the foundational YOLOv3 and RetinaNet pre-trained models to discern and appraise cracks on the railway surface. This model exhibited commendable recall and precision metrics when applied to a constrained dataset [3]. The FS-RSDD model uses prototype learning to estimate the defect probability of test samples, overcoming the limitations of supervised learning algorithms to achieve high-precision defect detection and localization with limited training samples [33]. [PERSON] et al., through the manipulation of acquired images from the railway, extracted precise positional information pertaining to the areas of rails necessitating polishing. They employed a machine vision system to discern regions requiring track grinding [34].