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.

在道路基础设施维护和保障道路安全中，有效检测道路危险是至关重要的。本研究评估了 YOLOv8，一种物体检测模型，在检测路面危险，如坑洞、下水道盖和人孔等方面的综合性能。通过与之前版本 YOLOv5 和 YOLOv7 的比较分析，强调了计算效率在各种应用中的重要性。本文探讨了 YOLOv8 的架构，并探索了旨在提高检测准确性的图像预处理技术，包括光照、道路类型、危险大小和类型的多样条件。此外，通过调整学习率、批量大小、锚点框大小和增强策略进行超参数调优实验，优化模型的性能。模型评估基于 Mean Average Precision (mAP)，这是一种广泛接受的物体检测性能指标。通过在多种测试场景下计算 mAP 分数，本研究评估了模型的稳健性和泛化能力，强调了 YOLOv8 在道路危险检测和基础设施维护中的重要性。