Enhanced Pothole Detection in Urban Environments Using YOLO-NAS with Adaptive Image Augmentation Techniques

Pothole detection in urban environments is a critical task for maintaining road infrastructure and ensuring vehicular safety. Recent advancements in deep learning, particularly the YOLO (You Only Look Once) framework, have shown promise in object detection tasks. However, achieving high accuracy and robustness in real-world conditions remains a challenge. This study introduces an enhanced pothole detection system utilizing the YOLO-NAS (Neural Architecture Search) model, optimized through adaptive image augmentation techniques. The YOLO-NAS model is fine-tuned on a custom dataset of urban road images, with various augmentation strategies applied to simulate different environmental conditions such as shadows, low light, and occlusions. These augmentations include but are not limited to, geometric transformations, photometric adjustments, and synthetic occlusion generation. The performance of the YOLO-NAS model is evaluated against standard YOLO models to demonstrate improvements in detection accuracy and robustness. Experimental results show that the YOLO-NAS model, combined with adaptive image augmentation, significantly outperforms traditional YOLO models. The enhanced system achieves a mean Average Precision (mAP) of 85.4%, a 12% improvement over the baseline YOLO model. The system demonstrates superior performance in detecting potholes under various challenging conditions, including low-light environments and occluded scenarios. The real-time processing capability of the model makes it suitable for integration into mobile and edge devices for practical urban deployment. Integrating YOLO-NAS with adaptive image augmentation techniques offers a robust solution for pothole detection in urban environments. This approach enhances detection accuracy and ensures consistent performance across diverse and challenging conditions, paving the way for more reliable and efficient road maintenance systems.