A Novel Deep Learning Approach for Enhanced Roadway Pothole Detection Using YOLOv8 Instance Segmentation Algorithms
DOI:
https://doi.org/10.22399/ijcesen.3285Keywords:
Instance Segmentation, YOLOv8 Architecture, Roadway Pothole Detection, Deep Learning Algorithms,, Autonomous Vehicle NavigationAbstract
Potholes pose a major risk to roadway safety and vehicle durability, necessitating timely detection and repair. With advancements in artificial intelligence, deep learning has become crucial for automating pothole detection and segmentation. Previous studies using CNN and Haar cascade methods have achieved accuracies up to 98.2%. This paper presents a novel approach leveraging the YOLOv8 architecture for instance segmentation, enhancing detection accuracy by capturing contextual and spatial relationships. The process involves data collection, annotation, preprocessing, and model training using datasets from Roboflow Universe and Kaggle. The model's performance is assessed through sensitivity, precision, recall, F1 score, and mean average precision (mAP). Experimental results indicate a significant improvement, achieving a 99.2% mAP in pothole detection and segmentation. These findings highlight the potential of YOLOv8 in advancing automated road maintenance, ensuring safer and more efficient transportation systems.
References
[1] Pan, Q., Gao, M., Wu, P., Yan, J., & Li, S. (2021). A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images. Sensors, 21(19), 6540. https://doi.org/10.3390/s21196540
[2] Wang, D., & Yang, S. X. (2022). Intelligent feature extraction, data fusion and detection of concrete bridge cracks: current development and challenges. Intelligence & Robotics, 2(4), 391-406. http://dx.doi.org/10.20517/ir.2022.25
[3] Xia, B., Luo, H., & Shi, S. (2022). Improved Faster R-CNN Based Surface Defect Detection Algorithm for Plates. Computational Intelligence and Neuroscience, 2022, 3248722. https://doi.org/10.1155/2022/3248722
[4] Xu, B., Wang, W., Falzon, G., Kwan, P., Guo, L., Chen, G., Tait, A., & Schneider, D. (2020). Automated cattle counting using Mask R-CNN in quadcopter vision system. Computers and Electronics in Agriculture, 171, 105300. https://doi.org/10.1016/j.compag.2020.105300
[5] Triki, A., Bouaziz, B., Gaikwad, J., & Mahdi, W. (2021). Deep Leaf: Mask R-CNN based leaf Detection and Segmentation from digitized herbarium specimen images. Pattern Recognition Letters, 150. https://doi.org/10.1016/j.patrec.2021.07.003
[6] Wan, S., & Goudos, S. (2019). Faster R-CNN for Multi-class Fruit Detection using a Robotic Vision System. Computer Networks, 168, 107036. https://doi.org/10.1016/j.comnet.2019.107036
[7] Padma, E., Rohith, D. N. V., & Sai Charan, E. V. (2022). Mask RCNN: Object Detection Approach using Machine Learning Techniques. Volume 13, Issue 03.
[8] Pan, Q., Gao, M., Wu, P., Yan, J., & Li, S. (2021). A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images. Sensors, 21(19), 6540. https://doi.org/10.3390/s21196540
[9] Sharma, R., Saqib, M., Lin, C. T., et al. (2022). A Survey on Object Instance Segmentation. SN Computer Science, 3, 499. https://doi.org/10.1007/s42979-022-01407-3
[10] Satti, S. K., Devi, K. S., Dhar, P., et al. (2022). Detecting potholes on Indian roads using Haar feature-based cascade classifier, convolutional neural network, and instance segmentation. Soft Computing, 26, 9141–9153. https://doi.org/10.1007/s00500-022-07265-8
[11] Zimmermann, R. S., Siems, J. N. (2019). Faster training of Mask R-CNN by focusing on instance boundaries. Computer Vision and Image Understanding, 188, 102795. https://doi.org/10.1016/j.cviu.2019.102795
[12] Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., & Wang, X. (2023). InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14408-14419). https://doi.org/10.1109/CVPR52729.2023.01385
[13] M. J. Carmel Mary Belida, A. Begum, S. Alex David, E. Kannan, K. Senthil and N. Ruth Naveena, "Predictive Modeling for Medical Insurance Malpractice Using Random Forest and XGBoost," 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 2024, pp. 1-5.
[14] Bai, R., Shen, F., Wang, M., et al. (2023). Improving Detection Capabilities of YOLOv8-n for Small Objects in Remote Sensing Imagery: Towards Better Precision with Simplified Model Complexity [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-3085871/v1
[15] Ganesh, P., Volle, K., Burks, T. F., & Mehta, S. (2019). Deep Orange: Mask R-CNN based Orange Detection and Segmentation. IFAC-PapersOnLine.
[16] Liu, Y., Mishra, N., Abbeel, P., & Chen, X. (2023). Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 7069-7075). London, United Kingdom. https://doi.org/10.1109/ICRA48891.2023.10160812
[17] R. M. Naidu and V. Nagaraju, "Efficient Online Medical Store Finding and Availability of Medicines Using SVM Compared over CNN with Improved Accuracy," 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2023, pp. 1-5.
[18] Bučko, B., Lieskovská, E., Zábovská, K., Zábovský, M. (2022). Computer Vision Based Pothole Detection under Challenging Conditions. Sensors.
[19] Amo-Boateng, M., Sey, N. E. N., Amproche, A. A., & Domfeh, M. K. (2022). Instance segmentation scheme for roofs in rural areas based on Mask R-CNN. The Egyptian Journal of Remote Sensing and Space Science, 25(2), 569-577. https://doi.org/10.1016/j.ejrs.2022.03.017
[20] Zhang, Q., Chang, X., & Bian, S. B. (2020). Vehicle-Damage-Detection Segmentation Algorithm Based on Improved Mask RCNN. IEEE Access, 8, 6997-7004. https://doi.org/10.1109/ACCESS.2020.2964055
[21] Knopp, L., Wieland, M., Rättich, M., & Martinis, S. (2020). A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data. Remote Sensing, 12(15), 2422. https://doi.org/10.3390/rs12152422
[22] S. Ashokkumar, S. M. Kumar, R. Rajaraman, D. Sugumaran and N. N. Saranya, "Comparative Analysis of Deep Learning Algorithms for Image Recognition in Medical Imaging," 2024 Second International Conference on Advances in Information Technology (ICAIT), Chikkamagaluru, Karnataka, India, 2024, pp. 1-6
[23] Bagheri, F., Tarokh, M. J., Ziaratban, M. (2021). Skin lesion segmentation from dermoscopic images by using Mask R-CNN, Retina-Deeplab, and graph-based methods. Biomedical Signal Processing and Control, 67, 102533. https://doi.org/10.1016/j.bspc.2021.102533
[24] Khan, M. A., Akram, T., Zhang, Y.-D., & Sharif, M. (2021). Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework. Pattern Recognition Letters, 143, 58-66. https://doi.org/10.1016/j.patrec.2020.12.015
[25] R. Sundar, M. Ganesan, M.A. Anju, M. Ishwarya Niranjana, & T. Surya. (2025). A Context-Aware Content Recommendation Engine for Personalized Learning using Hybrid Reinforcement Learning Technique. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.912
[26] Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.19
[27] Johnsymol Joy, & Mercy Paul Selvan. (2025). An efficient hybrid Deep Learning-Machine Learning method for diagnosing neurodegenerative disorders. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.701
[28] Kumari, S. (2025). Machine Learning Applications in Cryptocurrency: Detection, Prediction, and Behavioral Analysis of Bitcoin Market and Scam Activities in the USA. International Journal of Sustainable Science and Technology, 1(1). https://doi.org/10.22399/ijsusat.8
[29] Sivananda Hanumanthu, & Gaddikoppula Anil Kumar. (2025). Deep Learning Models with Transfer Learning and Ensemble for Enhancing Cybersecurity in IoT Use Cases. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1037
[30] Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.18
[31] G Nithya, R, P. K., V. Dineshbabu, P. Umamaheswari, & T, K. (2025). Exploring the Synergy Between Neuro-Inspired Algorithms and Quantum Computing in Machine Learning. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.2484
[32] Chui, K. T. (2025). Artificial Intelligence in Energy Sustainability: Predicting, Analyzing, and Optimizing Consumption Trends. International Journal of Sustainable Science and Technology, 1(1). https://doi.org/10.22399/ijsusat.1
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