ArticleScientific reports2024
YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- A Macro-to-Micro Framework for Bridge-Deck Pavement-Distress Inspection Using LiDAR-Based Screening and YOLOv8s Image Detection: A Case Study of the Jingzhou Yangtze River Highway Bridge and Public Benchmark Data.Sensors (Basel, Switzerland) · 2026Article
- SML-YOLO: a lightweight insulator defect detection algorithm for transmission lines under harsh weather conditions.Scientific reports · 2026Article
- Evaluating YOLO26s for Multi-Class Pavement Crack Detection: A Lightweight Approach for Sustainable Edge Deployment.Sensors (Basel, Switzerland) · 2026Article
- RDL-YOLO: an efficient algorithm for detecting road surface defects.Scientific reports · 2026Article
- Fs2PA: A Full-Scale Feature Synergistic Perception Architecture for Vehicular Infrared Object Detection via Physical Priors and Semantic Constraints.Sensors (Basel, Switzerland) · 2026Article
- A Code-Conforming Computer Vision Framework for Visual Inspection of Reinforced and Prestressed Concrete Bridges.Sensors (Basel, Switzerland) · 2026Article
- SDC-YOLOv8: An Improved Algorithm for Road Defect Detection Through Attention-Enhanced Feature Learning and Adaptive Feature Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- YOLO-ERCD: An Upgraded YOLO Framework for Efficient Road Crack Detection.Sensors (Basel, Switzerland) · 2026Article
- A multi-scale transformer-enhanced YOLO framework for unified road damage detection and boundary-aware segmentation.Frontiers in artificial intelligence · 2026Article
- An advanced three stage lightweight model for underwater human detection.Scientific reports · 2025Article
- Comparative Analysis of YOLO Series Algorithms for UAV-Based Highway Distress Inspection: Performance and Application Insights.Sensors (Basel, Switzerland) · 2025Article
- Deep spatial attention networks for vision-based pavement distress perception in autonomous driving.PloS one · 2025Article
- Article
- Identification of water-cooled wall ash accumulation based on AWGAM-YOLOv8n.Scientific reports · 2024Article
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2 authors.
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Abstract
Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale pavement distresses and high costs in detection task, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (pavement distress). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the large separable kernel attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road damages while reducing the computational load. Furthermore, we introduced lightweight shared convolution detection head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement distress.
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