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

Timely bridge-deck pavement inspection must cover large areas while preserving the image detail needed to recognize local distress. This study develops a macro-to-micro workflow with separate LiDAR screening and YOLOv8s image-detection branches. Airborne LiDAR data from the Jingzhou Yangtze River Highway Bridge supported deck extraction and geometric screening. Processing included statistical outlier removal, coordinate normalization, voxel sampling, local plane fitting, residual and normal-discrepancy screening, and spatial clustering. Deck extraction retained 179,914 of 311,784 representative points. Geometric screening based on residual and normal discrepancy identified 17,075 suspected points. Region filtering followed by bounding-box export yielded 423 points in seven candidate regions for targeted inspection. The 2025 inspection report documented pavement distress, and site personnel confirmed corresponding distress within the screened regions. The image branch used the official image-level splits of UAV-PDD2023. The epoch-146 checkpoint achieved the highest validation mAP50 and was evaluated on the official-test split. Official-test precision reached 0.85591, with a recall of 0.85882, mAP50 of 0.88556, and mAP50–95 of 0.58195. The model detected all six pavement-distress classes under the fixed benchmark protocol. The two branches support wide-area geometric screening and detailed image analysis for bridge-deck pavement inspection.