Application of YOLOv5s Algorithm for Real-Time Object Detection in Mobile Robot for Volcano Monitoring System
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Abstract
Indonesia, a country with 172 volcanoes and second after Japan for the most eruption events, should monitor and predict the volcano eruption to prevent the effect of this natural disaster. Therefore, we have developed a 4-wheeled mobile robot equipped with monitoring sensors and a Logitech camera for this purpose. The robot should have the ability to detect objects in this extreme environment to avoid collision while moving and monitoring the volcano’s physical parameters. It has been designed a deep machine learning of YOLOv5s algorithm for two objects mostly found at volcanoes such as trees and stones. After the training steps (object identification; dataset downloading (Google Chrome Extension and Open Images v6); image labeling (LabeImg); augmentation process (blur and rotation)) had been carried out, the images of the object then trained in three model variation which resulted in: mAP_0.5 = 51.9%, mAP_0.5:0.95 = 28.6%, 58% of precision and 50% recall with 12 minutes and 33 seconds of training time for the first model (batch=16 and epochs=100); mAP_0.5 = 59.7%, mAP_0.5:0.95 = 36.3%, 74% of precision and 54% recall with 36 minutes and 4 seconds of training time for the second model (batch=16 and epochs=300); mAP_0.5 = 59.9%, mAP_0.5:0.95 = 37.6%, 80% of precision and 55% recall with one hour and 25 seconds of training time for the last one (batch=16 and epochs=500) as the best model of these variations. Furthermore, these results were displayed for all test images for the best model.
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Evita, M. (2024). Application of YOLOv5s Algorithm for Real-Time Object Detection in Mobile Robot for Volcano Monitoring System. Indonesian Journal of Physics, 35(1), 8-16. https://doi.org/10.5614/itb.ijp.2024.35.1.2
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References
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[2] M. Djamal, et al., Development of a Low-cost Mobile Volcano Early Warning System, J. Tech. Sci., 1, 84, 2017.
[3] M. Evita, et al., (Fixed-mode of mobile monitoring system for Indonesian volcano), Proc. 4th Int. Conf. on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), (Nov. 2-3, 2015, Bandung, Indonesia), 278, 2015.
[4] M. Evita, et al., Development of Volcano Early Warning System for Kelud Volcano, JETS ITB, 53, 2021.
[5] M. Evita, et al., (Mobile robot deployment experiment for mobile mode of mobile monitoring system for Indonesian volcano), Proc. of Int. Conf. on Technology and Social Science, (May, 10-11, 2017, Kiryu, Japan), Keynote Lecture, 2017.
[6] M. Evita, et al., (Development of a robust mobile robot for volcano monitoring application), Proc. of the 9th Int. Conf. on Theoretical and Applied Physics (ICTAP), (Sep., 26-28, 2019, Lampung, Indonesia), 1572, 2019.
[7] M. Evita, et al., Photogrammetry using Intelligent-Battery UAV in Different Weather for Volcano Early Warning System Application, J. Phys.: Conf. Ser., 1772, 012017, 2021
[8] A. Zakiyyatuddin, et al., Geospatial Survey Analysis for 3D Field and Building Mapping using DJI Drone and Intelligent Flight Battery, J. Phys.: Conf. Ser., 1772, 012015, 2021.
[9] V. F. Amaliya, et al., Development of IoT-Based Volcano Early Warning System, J. Phys.: Conf. Ser., 1772, 012009, 2021.
[10] M. Evita, S. T. Mustikawati, and M. Djamal, Design of Real-Time Object Detection in Mobile Robot for Volcano Monitoring Application, J. Phys.: Conf. Ser., 2243, 012038, 2022.
[11] A. Ohya, A. Kosaka and A. Kak, (Vision-based navigation of mobile robot with obstacle avoidance by single camera vision and ultrasonic sensing), Proc. Of the 1997 IEEE/RSJ Int. Conf. on Intelligent Robot and Systems, Innovative Robotics for Real-World Applications IROS ’97, (Sep. 7-11, 1997, Grenoble, France), 704, 1997.
[12] T. Xinchi, et al., (A Research on Intelligent Obstacle Avoidance for Unmanned Surface Vehicles), 2018 Chinese Automation Congress (CAC), (Nov. 30 – Dec. 2, 2018, Xi’an, China), 1431, 2018.
[13] A. Gonzalez-Garcia, et al., (A 3D Vision Based Obstacle Avoidance Methodology for Unmanned Surface Vehicles), XXI Congreso Mexicano de Robótica, (Nov. 19, 2019, Colima, Mexico), 2019.
[14] D. Fridovich-Keil, et al., (Probabilistically Safe Robot Planning with Confidence-Based Human Predictions), 2018 IEEE International Conference on Robotic and Automation ICRA, (May 21-26, 2018, Brisbane, Australia), 387, 2018.
[15] Y. Peng, et al., (Obstacle detection and obstacle avoidance algorithm based on. 2-d lidar), 2015 IEEE International Conference on Information and Automation, (Aug. 8-10, 2015, Yunnan, China), 1648, 2015.
[16] J. Yan, et al., YOLOv5-Ytiny: A Miniature Aggregate Detection and Classification Model, Electronics, 10, 1711, 2021.
[17] F. Yang, et al., Deep Learning for smart manufacturing: Methods and applications, Appl. Sci., 10, 2361, 2020.
[18] R. Devnita, Melanic and Fulvic Andisols in Volcanic Soils derived from some Volcanoes in West Java, Indonesian Journal of Geology, 7, 227, 2012.
[19] D Castells, M. F. Rodrigues and M. H. du Buf, (Obstacle Detection and Avoidance on Sidewalk), Proc. of the International Conference on Computer Vision Theory and Applications, (May 17-21, 2010, Angers, France), 235, 2010.
[20] R. Girshick, et al., (Rich feature hierarchies for accurate object detection and semantic segmentation), Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, (Jun. 23-28, 2014, OH, USA), 580, 2014.
[21] S. Ren, et al., Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in neural information processing system, 91, 2015.
[22] J. Du, Understanding of Object Detection Based on CNN Family and YOLO, J. Phys.: Conf.Ser., 1004, 1, 2018.
[23] A. Kuznetsova, et al., The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale, Int. J. of Computer Vision, 128, 2020.
[24] I. Namatevs, K. Sudars and I. Polaka, Automatic data labeling by neural networks for the counting of objects in videos, Procedia Computer Science, 149, 151, 2019.
[25] C. Shorten and T. G. Khoshgoftaar, A survey on Image Data Augmentation for Deep Learning. J. of Big Data, 6, 60, 2019.
[26] Y. Xu and R. Goodacre, On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning, J. of Analysis and Testing, 2, 2018.
[27] J. Redmon, et al., (You Only Look Once: Unified, Real-Time Object Detection), 2016 IEEE Conf. on Computer Vision and Patern Recognition (CVPR), (Jun. 27-30, 2016, NV, USA), 779, 2016.
[28] J. Yu, et al., (UnitBox: An advanced object detection network), Proceedings of the 24th ACM international conference on Multimedia, (Oct. 15-19, 2016, Amsterdam, Netherlands), pp. 516, 2016.
[29] H. Rezatofighi, et al., (Generalized in- tersection over union: A metric and a loss for bounding box regression), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (June 15-20, 2019, CA, USA), 658, 2019.
[30] Z. Zheng, et al., (Distance-IoU Loss: Faster and better learning for bounding box regression), Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), (Feb. 7-12, 2020, New York, USA), 12993, 2020.
[31] M. Andrychowicz, et al., (Learning to learn by gradient descent by gradient descent), Proc. Of the 30th Int. Conf. on Neural Information Processing System, (Dec. 5-10, 2016, Barcelona, Spain), 3988, 2016.
[32] P. Netrapalli, Stochastic Gradient Descent and Its Variants in Machine Learning, Journal of the Indian Institute of Science, 99, 2019.
[33] N. S. Keskar, et al., (On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima), 5th Int. Conf. on Learning Representation, ICLR, (Apr. 24-26, 2017, Toulon, France), 149804, 2017.
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