A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)
Abstract 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
Highlights An intelligent pavement disease inspection system that utilizes an improved version of YOLO and OSNet is proposed. The space-to-depth strategy was introduced in the backbone of the YOLOv5s model. OSNet was developed for feature extraction and filter out repeated distress images. The proposed methods were integrated into an intelligent pavement disease inspection vehicle. Field experiments confirmed the real-time and high efficiency of the system.
Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)
Abstract 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
Highlights An intelligent pavement disease inspection system that utilizes an improved version of YOLO and OSNet is proposed. The space-to-depth strategy was introduced in the backbone of the YOLOv5s model. OSNet was developed for feature extraction and filter out repeated distress images. The proposed methods were integrated into an intelligent pavement disease inspection vehicle. Field experiments confirmed the real-time and high efficiency of the system.
Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)
Li, Jiale (author) / Yuan, Chenglong (author) / Wang, Xuefei (author)
2023-08-13
Article (Journal)
Electronic Resource
English
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