  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.
