ENEBELI, Perpetua Ogugua

Manual pavement inspection methods are slow, subjective, and often inconsistent, leading to delayed maintenance and increased road deterioration. This study was carried out to develop an automated, image-based system capable of detecting and classifying visible defects in flexible pavements using machine learning. The objectives of the study were to review existing pavement inspection techniques, collect and preprocess pavement image data, and design and train a model capable of identifying pavement failures accurately. The study was with the aim of improving the speed, objectivity, and reliability of pavement condition assessments. A dataset of pavement images was obtained from the Edo State Ministry of Works, field surveys, and public sources. The images were annotated in YOLO format and augmented by flipping, rotation, cropping, and brightness adjustment. The YOLOv8 object detection model, implemented in Python using TensorFlow, PyTorch, and OpenCV, was trained on Google Colab with an NVIDIA T4 GPU. Training was performed at varying epochs (50, 100, and 200) and hyperparameters to optimize detection performance. The model’s accuracy was evaluated using mean Average Precision (mAP) and recall metrics to assess its ability to detect cracks, potholes, and rutting in flexible pavements. Results showed that the model achieved a mean Average Precision (mAP₅₀) of 0.68 and recall above 0.80 for visible defects such as potholes and alligator cracking, at a confidence level of 0.5. The model was less effective in detecting faint, low-contrast linear cracks. This study concluded that YOLOv8-based models can effectively automate pavement distress detection, providing a faster and more reliable alternative to manual inspection. It is recommended that future work expand the dataset and explore enhanced training strategies to improve the detection of subtle linear cracks.