YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes
It addresses road safety and infrastructure maintenance by providing a more efficient detection method, but it is incremental as it applies an existing model to a specific domain.
This paper evaluated YOLOv8 for detecting road hazards like potholes, sewer covers, and manholes, comparing it to YOLOv5 and YOLOv7, and found that YOLOv8 achieved improved detection accuracy as measured by Mean Average Precision (mAP) scores across diverse conditions.
Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road safety. This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is conducted, emphasizing the importance of computational efficiency in various applications. The paper delves into the architecture of YOLOv8 and explores image preprocessing techniques aimed at enhancing detection accuracy across diverse conditions, including variations in lighting, road types, hazard sizes, and types. Furthermore, hyperparameter tuning experiments are performed to optimize model performance through adjustments in learning rates, batch sizes, anchor box sizes, and augmentation strategies. Model evaluation is based on Mean Average Precision (mAP), a widely accepted metric for object detection performance. The research assesses the robustness and generalization capabilities of the models through mAP scores calculated across the diverse test scenarios, underlining the significance of YOLOv8 in road hazard detection and infrastructure maintenance.