YOLO: A Competitive Analysis of Modern Object Detection Algorithms for Road Defects Detection Using Drone Images

Abstract

Efficient identification of road defects is a critical concern for road safety and infrastructure upkeep. This research employs drone-captured imagery and advanced object detection algorithms to expedite defect recognition, with a specific focus on determining the optimal algorithm for prompt and precise detection. The importance of timely road defect detection, crucial for mitigating potential hazards, remains central. A comprehensive comparative analysis of contemporary object detection algorithms, encompassing YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, and YOLOv7. The results of this study highlight YOLOv7 as the most efficient, with a notable mAP of 68.3%, closely followed by YOLOv5l (66.8%), YOLOv5m (66.3%), YOLOv5x (66%), and YOLOv5s (63%). The integration of drone-derived imagery, capturing distinct gradients, significantly enhances defect detection accuracy. Beyond road safety, this study offers valuable insights to computer vision and machine learning practitioners. By bridging technological innovation with practical implementation, it holds potential to advance road safety and transportation infrastructure quality and the use of revolutionary drone technology.

Keywords

• Convolution Neural Network, YOLO, drone images, CSPDarknet, Input Size