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Title	Real-Time Pothole Detection Using Yolov7: An Efficient Deep Learning Approach For Road Safety And Maintenance
ID_Doc	44425
Authors	Bhosale S.B.; Ponnusamy S.
Year	2025
Published	2025 International Conference on Data Science and Business Systems, ICDSBS 2025
DOI	http://dx.doi.org/10.1109/ICDSBS63635.2025.11031999
Abstract	Potholes on roadways pose a significant hazard to vehicular safety and infrastructure, particularly in developing regions. Traditional object detection models have shown limitations in either processing speed or adaptability to diverse environmental conditions, hindering their effectiveness in real-time applications. This paper presents a comprehensive evaluation of the YOLOv7 object detection model for automated pothole detection. Leveraging its enhanced architectural design, YOLOv7 demonstrates superior performance in both accuracy and inference speed compared to established models such as Faster R-CNN, SSD, and earlier YOLO versions. Experiments conducted on a dataset encompassing varied road conditions-spanning different lighting, weather, and surface textures-showcase the model's robust generalization capabilities. The results highlight YOLOv7 as a promising solution for integration into real-time intelligent transportation systems and autonomous vehicle platforms, contributing toward safer and more efficient road maintenance strategies. © 2025 IEEE.
Author Keywords	Deep Learning; Edge Computing; Neural Networks; Object Detection; Pothole Detection; Real-Time Processing; Road Maintenance; Smart City Infrastructure; Transportation Safety; YOLOv7

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