Smart Road Pothole Detection using YOLOv8 and Computer Vision

This This project presented a deep learning-based approach for detecting road surface damages such a potholes and cracks using image data. The primary objective was to design and train a model tha can accurately identify road damages and evaluate its performance on unseen images without relyin on external hardware systems. The implementation of the YOLOv8 object detection mode demonstrated that deep learnin techniques can effectively detect potholes and cracks with good accuracy an reliability. The mode was trained using a labeled dataset of road images and successfully tested on new inputs, showin its ability to generalize beyond the training data The results obtained from the model indicate that it can accurately identify damaged regions an provide confidence scores for each detection. Additionally, the system includes a dimension estimation feature, which provides approximate measurements of pothole size based on boundin box analysis. This adds practical value to the model by offering more than just detection.

 [1] Review Paper on Data Mining Techniques in CRM of Pharmaceutical Industry [2] 1. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [3] . A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020. [4] . G. Jocher et al., “Ultralytics YOLOv8: State-of-the-Art Object Detection Model,”Ultralytics Documentation, 2023 [5] Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42(6), 505–512. [6] Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. [7] Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A Brief Measure for Assessing Generalized Anxiety Disorder. Archives of Internal Medicine, 166(10), 1092–1097. [8] Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213.