Intelligent detection method for metro track defect targets based on improved YOLOv8s algorithm

For the problems of small-sized targets, sample imbalance, low detection accuracy, and high real-time requirements in track state detection, a detection method for metro track defect targets fusing a small target detection algorithm and the Focal Loss function was proposed. The TrackScanner defect dataset was constructed through data cleaning and image annotation. For the problems of "small target samples" and "unbalanced positive and negative samples" existing in the dataset, targeted improvements were made on the basis of the YOLOv8s algorithm. To address the problem of "small target samples", a lightweight dual attention mechanism module, an involution module, and a small target detection head with a detection scale of 160 × 160 were added. The attention of the algorithm to key details was enhanced through joint improvement measures. To address the problem of "unbalanced positive and negative samples", the Focal Loss function was introduced. This improvement regulated the predicted result value of the loss function through a weight factor and a loss factor. Through model training and ablation experiments, the effects and advantages of the proposed algorithm in the field of track state detection were compared and analyzed. Research results indicate that the mAP₅₀, F₁, and defection efficiency of the improved YOLOv8s algorithm are 71.70%, 73.43%, and 46.84 frame·s^-1, respectively, and the detection accuracies for defect targets such as missing fastener, reversed fastener, shifted fastener, broken fastener, foreign object on the track bed, and rail surface abrasion are 82.6%, 65.8%, 72.3%, 25.3%, 95.6%, and 88.6%, respectively. This research is applicable to the detection work of metro track defect targets, can effectively enhance the work efficiency of track inspection personnel, and is crucial for ensuring the safe operation of track trains.