earticle

영어

Pothole detection remains a critical challenge in road maintenance and safety management, as potholes deteriorate road surfaces, compromise vehicle safety, and increase maintenance costs. Traditional pothole detection methods relying on manual inspection or simple image processing are often labor-intensive, prone to human error, and lack adaptability to varying road conditions. Meanwhile, modern approaches utilizing single-stage object detectors such as YOLO variants have provided real-time detection capabilities but tend to suffer in accurately localizing potholes at higher Intersection over Union (IoU) thresholds, especially when faced with the irregular shapes and scale variability characteristic of real-world potholes. To overcome these limitations, a multi-stage detection framework based on Cascade Region-based Convolutional Neural Network (Cascade R-CNN) with a ResNet-50 backbone and a Feature Pyramid Network (FPN) was developed. This framework employs progressive bounding box refinement through multiple detection stages with increasingly strict IoU thresholds, resulting in improved localization precision. The model was trained and evaluated on a meticulously curated dataset of more than 30,000 images featuring diverse pothole instances. It achieves a mean Average Precision (mAP) of 0.653 across IoU thresholds from 0.5 to 0.95, surpassing the baseline Faster RCNN by 4.3 points and outperforming YOLOv8 by 5 points. On an NVIDIA RTX 4090 GPU, the proposed model runs at approximately 80–90 frames per second, which enables nearreal- time execution and renders it practical for integration into automated road inspection and maintenance systems. These results indicate that the proposed Cascade R-CNN framework offers a robust and effective solution for high-accuracy pothole detection, addressing the shortcomings of existing detection methods in complex road environments.