Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)

Pavements function as protective layers for roads and require frequent inspection and maintenance throughout their service life. This paper describes an intelligent pavement distress inspection system that uses an enhanced version of the ‘you only look once’ (YOLO) model and an omni-scale network (OSNet) to instantly capture road surface distress images and their precise locations. The YOLO model was evaluated on a dataset comprising 9749 pavement distress images, with the detected distress serving as an input for feature extraction and instance-level recognition through OSNet. The OSNet model achieved a mean average precision (mAP) of 99.4% for a dataset containing 398 individual distress instances. The proposed methods were successfully integrated into a pavement distress inspection vehicle. Field experiments demonstrated the real-time capability and high efficiency of the system, with significant improvement in road maintenance inspection efficiency