Improved YOLOv8 for rail squat detection and identification

Rail transport plays a vital part in the country's economy by ensuring the safe movement of both goods and passengers. Therefore, maintaining rail safety through consistent surface defect inspection is extremely importan. However, squat defect detection on rail surfaces faces considerable difficulties due to weather impacts, lighting changes, and variations in image contrast. These challenges hinder the accuracy and reliability of traditional inspection methods. To solve this problem, this study proposes an improved YOLOv8 model for the identification and classification of squat defects. The methodology involves capturing images of the rail track, preprocessing them to enhance image quality, labeling squat defects for training purposes, and training the proposed model using the labeled dataset. The improved YOLOv8 model incorporates enhancements such as multi-scale convolution modules and attention mechanisms to improve feature extraction and defect recognition. Experimental results demonstrate the effectiveness of the proposed method, achieving an impressive accuracy of 0.92 in detecting and categorizing squat defects. These findings highlight the potential of the proposed approach to enhance railway safety by providing a reliable and efficient solution for rail surface inspection.