Road Damage Detection for Autonomous Driving Vehicles using YOLOv8 and Salp Swarm Algorithm
Keywords:
Hyperparameter optimization, Object detection , Salp swarm pptimization, YOLOv8Abstract
Road accidents are one of the leading causes of death and serious injury in Malaysia, often resulting from human errors and poor road conditions. Autonomous vehicles aim to reduce accidents by mitigating human errors. Therefore, improving the road damage detection model in autonomous vehicles is crucial for enhancing their decision-making capabilities and reducing road accidents. Finding suitable sets of hyperparameters for this task is time-consuming. Consequently, this paper proposes a method to improve the detection accuracy of You Only Look Once version 8 (YOLOv8) using Salp Swarm Algorithm (SSA) for hyperparameter optimization, focusing on eight key parameters. The model is trained using the Czech data in Road Damage Dataset RDD2022 from the Crowdsensing-based Road Damage Detection Challenge (CRDDC’2022), with 80% of the data used for training and 20% for validation. The YOLOv8n model is trained with SSA on the RDD2022 dataset, specifically using data from India and China, to find the optimal parameters. The model is then retrained using the hyperparameters identified by SSA. The YOLOv8 models optimized using SSA are compared with the original YOLOv8 and other YOLO versions (YOLOv5, YOLOv9, and YOLOv10), demonstrating a 3.5% improvement in accuracy after hyperparameter optimization in detecting road damage.References
[1] World Health Organization, Road Traffic Injuries, World Health Organization, https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries, 2023 (accessed 01.01.2024).
[2] Kementerian Pengangkutan Malaysia, Kemalangan dan Kematian Jalan Raya di Malaysia. https://www.mot.gov.my/my/land/safety/road-accident-and-facilities (accessed 15.05.2024).
[3] Kementerian Pengangkutan Malaysia, Indeks Kematian Jalan Raya Malaysia. https://www.mot.gov.my/my/land/safety/malaysia-road-fatalities-index (accessed 15.05.2024).
[4] Polis Diraja Malaysia (PDRM), Statistik Kemalangan Jalan Raya mengikut jenis kemalangan Dan Kecederaan Mengikut negeri - Set Data - MAMPU. https://archive.data.gov.my/data/ms_MY/dataset/statistik-kemalangan-jalan-raya-mengikut-jenis-kemalangan-dan-kecederaan-mengikut-negeri (accessed 15.05.2024).
[5] B. R. Kiran et al., Deep reinforcement learning for autonomous driving: a survey, IEEE Transactions on Intelligent Transportation Systems, 23(6), 2022, 4909-4926.
[6] P. Penmetsa, P. Sheinidashtegol, A. Musaev, E. K. Adanu, and M. Hudnall, Effects of the autonomous vehicle crashes on public perception of the technology, IATSS Research, 45(4), 2021, 485-492.
[7] P. Viola and M. J. Jones, Robust real-time face detection, International Journal of Computer Vision, 57(2), 2004, 137-154.
[8] N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, USA, I, 2005, 886-893.
[9] P. Felzenszwalb, D. McAllester, and D. Ramanan, A discriminatively trained, multiscale, deformable part model, 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2008), Anchorage, USA, 2008, 1-8.
[10] X. Wu, D. Sahoo, and S. C. H. Hoi, Recent advances in deep learning for object detection, Neurocomputing, 396, 2020, 39-64,
[11] R. Girshick, J. Donahue, T. Darrell, J. Malik, U. C. Berkeley and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, USA, 1, 2014, 5000.
[12] K. He, X. Zhang, S. Ren and J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 2015, 1904-1916.
[13] R. Girshick, Fast R-CNN, Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015, 1440-1448.
[14] S. Ren, K. He, R. Girshick and J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, http://image-net.org/challenges/LSVRC/2015/results, 2015 (accessed 06.11.2022)
[15] W. Liu et al., SSD: Single shot multibox detector, Lecture Notes in Computer Science, 9905, 2016, 21-37.
[16] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, You Only Look Once: Unified, Real-Time Object Detection, http://arxiv.org/abs/1506.02640, 2015.
[17] S. Naddaf-Sh, M. M. Naddaf-Sh, A. R. Kashani and H. Zargarzadeh, An Efficient and scalable deep learning approach for road damage detection, Proceedings of 2020 IEEE International Conference on Big Data (Big Data 2020), Atlanta, USA, 2020, 5602-5608.
[18] T. Y. Lin, P. Goyal, R. Girshick, K. He and P. Dollar, Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2017, 2999-3007.
[19] A. Alfarrarjeh, D. Trivedi, S. H. Kim and C. Shahabi, A deep learning approach for road damage detection from smartphone images, Proceedings of 2018 IEEE International Conference on Big Data (Big Data 2018), Seattle, USA, 2018, 5201-5204.
[20] Y. J. Wang, M. Ding, S. Kan, S. Zhang and C. Lu, Deep Proposal and Detection Networks for Road Damage Detection and Classification, Proceedings of 2018 IEEE International Conference on Big Data (Big Data 2018), Seattle, USA, 2018, 5224-5227.
[21] J. Zhu, J. Zhong, T. Ma, X. Huang, W. Zhang and Y. Zhou, Pavement distress detection using convolutional neural networks with images captured via UAV, Automation in Construction, 133, 2022, 103991.
[22] A. M. Roy and J. Bhaduri, DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and swin-transformer prediction head-enabled YOLOv5 with attention mechanism, Advanced Engineering Informatics, 56, 2023, 102007.
[23] V. Mandal, L. Uong and Y. Adu-Gyamfi, Automated road crack detection using deep convolutional neural networks, Proceedings of 2018 IEEE International Conference on Big Data (Big Data 2018), Seattle, USA, 2018, 5212–5215.
[24] L. Ale, N. Zhang and L. Li, Road damage detection using retinanet, Proceedings of 2018 IEEE International Conference on Big Data, (Big Data 2018), Seattle, USA, 2018, 5197-5200.
[25] Y. Zhang, H. Zhang, Q. Huang, Y. Han and M. Zhao, DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects, Expert Systems with Applications, 241, 2024, 122669.
[26] Z. Ge, S. Liu, F. Wang, Z. Li and J. Sun, YOLOX: Exceeding YOLO Series in 2021, 1–7, 2021.
[27] A. Bochkovskiy, C. -Y. Wang and H. -Y. M. Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection, http://arxiv.org/abs/2004.10934, 2020.
[28] M. Hussain, YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection, Machines, 11(7), 2023, 677.
[29] Ultralytics, Ultralytics YOLOv8 Docs, https://docs.ultralytics.com, 2023 (accessed 22.04.2024).
[30] D. Arya et al., Global road damage detection: State-of-the-art solutions, Proceedings of 2020 IEEE International Conference on Big Data (Big Data 2020), Atlanta, USA, 2020, 5533–5539.
[31] V. Hegde, D. Trivedi, A. Alfarrarjeh, A. Deepak, S. Ho Kim and C. Shahabi, Yet another deep learning approach for road damage detection using ensemble learning, Proceedings of 2020 IEEE International Conference on Big Data (Big Data 2020), Atlanta, USA, 2020, 5553-5558.
[32] T. Ho, Target detection algorithm for autonomous vehicles in road scenes, 5th International Conference on Computer Vision, Image and Deep Learning, (CVIDL 2024), Zhuhai, China, 2024, 1426-1431.
[33] Y. Ding, Enhanced YOLO optimization study for road sign detection in autonomous driving, 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL 2024), Zhuhai, China, 2024, 1316-1320.
[34] Y. Li et al., RDD-YOLO: Road damage detection algorithm based on improved you only look once version 8, Applied Sciences 2024, 14(8), 2024, 3360.
[35] J. Bergstra and Y. Bengio, Random search for hyper-parameter optimization, Journal of Machine Learning Research, 13, 2012, 281-305.
[36] A. H. Victoria and G. Maragatham, Automatic tuning of hyperparameters using Bayesian optimization, Evolving Systems, 12(1), 2021, 217-223.
[37] R. Hossain and D. D. Timmer, Machine learning model optimization with hyper parameter tuning approach, Global Journal of Computer Science and Technology, 21(2), 2021, 7-13.
[38] J. H. Holland, Genetic algorithms, Scientific American, 267(1), 1992, 66-72.
[39] R. Storn and K. Price, Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11(4), 1997, 341-359.
[40] S. Mirjalili, S. M. Mirjalili and A. Lewis, Grey wolf optimizer, Advances in Engineering Software, 69, 2014, 46-61.
[41] S. Mirjalili and A. Lewis, The whale optimization algorithm, Advances in Engineering Software, 95, 2016, 51-67.
[42] G. Yang, A modified particle swarm optimizer algorithm, 8th International Conference on Electronic Measurement and Instruments (ICEMI), Xian, China, 2007, 2675-2679.
[43] L. Xu, W. Yan and J. Ji, The research of a novel WOG-YOLO algorithm for autonomous driving object detection, Scientific Reports, 13(1), 2023, 1-13.
[44] J. Kolluri and R. Das, Intelligent multimodal pedestrian detection using hybrid metaheuristic optimization with deep learning model, Image and Vision Computing, 131, 2023, 104628.
[45] P. Singh, S. Chaudhury and B. K. Panigrahi, Hybrid MPSO-CNN: Multi-level particle swarm optimized hyperparameters of convolutional neural network, Swarm and Evolutionary Computation, 63, 2021, 100863.
[46] S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris and S. M. Mirjalili, Salp swarm algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, 114, 2017, 163-191.
[47] H. Faris, S. Mirjalili, I. Aljarah, M. Mafarja and A. A. Heidari, Salp swarm algorithm: Theory, literature review, and application in extreme learning machines, Studies in Computational Intelligence, 811, 2020, 185-199.
[48] H. Xie, L. Zhang, and C. P. Lim, Evolving CNN-LSTM models for time series prediction using enhanced grey wolf optimizer, IEEE Access, 8, 2020, 161519-161541.
[49] G. Atteia, N. Abdel Samee, E. S. M. El-Kenawy and A. Ibrahim, CNN-Hyperparameter optimization for diabetic maculopathy diagnosis in optical coherence tomography and fundus retinography, Mathematics, 10(18), 2020, 3274.
[50] E. Emary, H. M. Zawbaa and A. E. Hassanien, Binary grey wolf optimization approaches for feature selection, Neurocomputing, 172, 2016, 371-381.
[51] D. Arya, H. Maeda, S. K. Ghosh, D. Toshniwal and Y. Sekimoto, RDD2022: A Multi-National Image Dataset for Automatic Road Damage Detection, https://datasetninja.com/road-damage-detector, 2022 (accessed 29.05.2024).
[52] R. Kaur and S. Singh, A comprehensive review of object detection with deep learning, Digital Signal Processing, 132, 2023,103812.
[53] C. -Y. Wang, I. -H. Yeh and H. -Y. M. Liao, YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information, https://arxiv.org/abs/2402.13616v2, 2024 (accessed 25.06.2024).
[54] A. Wang et al., YOLOv10: Real-Time End-to-End Object Detection, https://arxiv.org/abs/2405.14458v1, 2024 (accessed 25.06.2024)
[55] H. Lou et al., DC-YOLOv8: Small-size object detection algorithm based on camera sensor, Electronics, 12(10), 2023, 2323.
[56] H. Maeda, T. Kashiyama, Y. Sekimoto, T. Seto, and H. Omata, Generative adversarial network for road damage detection, Computer-Aided Civil and Infrastructure Engineering, 36(1), 2021, 47-60.
[57] P. Ren, Y. Xiao, X. Chang, P. Huang, Z. Li, X. Chen and X. Wang, A comprehensive survey of neural architecture search: challenges and solution, ACM Computing Surveys, 54(4), 2021, 1-34.
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