Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework

• Autorzy: Toan Nguyen Quoc

Identyfikatory

DOI

10.35784/jcsi.2896

• Języki publikacji: EN

Abstrakty

EN

There is a great range of spectacular coral reefs in the ocean world. Unfortunately, they are in jeopardy, due to an overabundance of one specific starfish called the coral-eating crown-of-thorns starfish (or COTS). This article provides research to deliver innovation in COTS control. Using a deep learning model based on the You Only Look Once version 5 (YOLOv5) deep learning algorithm on an embedded device for COTS detection. It aids professionals in optimizing their time, resources, and enhances efficiency for the preservation of coral reefs worldwide. As a result, the performance over the algorithm was outstanding with Precision: 0.93 - Recall: 0.77 - F1score: 0.84.

Słowa kluczowe

EN

deep learning; computer vision; YOLO; embedded system

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Journal of Computer Sciences Institute
• Rocznik: 2022
• Tom: Vol. 23
• Strony: 105--111
• Opis fizyczny: Bibliogr. 25 poz., fig., tab.

Twórcy

autor

Toan Nguyen Quoc

• Department of Electronic and Electrical Engineering, Hongik University, Wausan-ro 94, Mapo-gu, Seoul, South Korea

Bibliografia

• 1. L. Jiajun, K. Brano, M. Ross, D. Brendan, M. Torsten, C. Joey, S. Andy, H. Nic, V. R. Karl, T. S. Lachlan, A. A. David, A. A. Mohammad, C. Geoffrey, B. Russ, M. Peyman, S. Daniel, D. Tim, E.M. Kemal, W. Martin, M. Megha, The CSIRO Crown-of-Thorn Starfish Detection Dataset, arXiv 2021, https://doi.org/10.48550/arXiv.2111.14311
• 2. W. Junlong, K. Wei, Z. Wei, H. Fengbiao, T. Xuefeng, W. Qiong, Helmet Detection Algorithm Based on the Improved YOLOv5 and Dynamic Anchor Box Matching, Proceedings of the IEEE International Conference on Emergency Science and Information Technology (ICESIT) (2021) 79-83, http://dx.doi.org/10.1109/ICESIT53460.2021.9696525.
• 3. Y. Zhong, J. Wang, J. Peng, L. Zhang, Anchor Box Optimization for Object Detection, Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2020) 1275-1283, http://dx.doi.org/10.1109/WACV45572.2020.9093498.
• 4. T. F. Dima, M. E. Ahmed, Using YOLOv5 Algorithm to Detect and Recognize American Sign Language, Proceedings of the International Conference on Information Technology (ICIT) (2021) 603-607, http://dx.doi.org/10.1109/ICIT52682.2021.9491672.
• 5. G. Verma, Y. Gupta, A. M. Malik, B. Chapman, Performance Evaluation of Deep Learning Compilers for Edge Inference, Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (2021) 858-865, http://dx.doi.org/10.1109/IPDPSW52791.2021.00128.
• 6. T. Zhi, S. Chunhua, C. Hao, H. Tong, FCOS: Fully Convolutional One-Stage Object Detection, arXiv 2019, https://doi.org/10.48550/arXiv.1904.01355.
• 7. L. Wei, A. Dragomir, E. Dumitru, S. Christian, R. Scott, F. Cheng-Yang, B. C. Alexander, SSD: Single Shot MultiBox Detector, Lecture Notes in Computer Science, 2016, https://doi.org/10.1007/978-3-319-46448-0_2.
• 8. Z. Ni, J. Chen, N. Sang, C. Gao, L. Liu, Light YOLO for high-speed gesture recognition, Proceedings of The 2018 25th IEEE International Conference on Image Processing (ICIP) (2018) 3099-3103, http://dx.doi.org/10.1109/ICIP.2018.8451766.
• 9. Aleena, S. Ayesha, J. Tauseef, U.K. Asif, Small Object Detection using Deep Learning, arXiv 2022, https://doi.org/10.48550/arXiv.2201.03243.
• 10. G. Han, J. G. Lee, K. T. Lim, D. H. Choi, Design of a scalable and fast YOLO for edge-computing devices, Sensors 20(23) (2020) 6779-6794, https://doi.org/10.3390/s20236779.
• 11. Liang, S. Wu, K. Xu, J. Hao, Butterfly detection and classification based on integrated YOLO algorithm, arXiv 2020, https://doi.org/10.48550/arXiv.2001.00361.
• 12. Shaobin, L. Wei, Embedded System Real-Time Vehicle Detection based on Improved YOLO Network, Proceedings of the IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (2019) 1400-1403, http://dx.doi.org/10.1109/IMCEC46724.2019.8984055.
• 13. Y. Zhu, C. Yao, X. Bai, Scene text detection and recognition: recent advances and future trends, Frontiers of Computer Science 10 (2016) 19-36, http://dx.doi.org/10.1007/s11704-015-4488-0.
• 14. Q. Lu, Y. Yuan, Improved YOLO Algorithm for Object Detection in Traffic Video, Proceedings of the International Conference in Communications, Signal Processing and Systems (2019) 1647-1655, http://dx.doi.org/10.1007/978-981-13-9409-6_198.
• 15. R. Shaoqing, H. Kaiming, G. Ross, S. Jian, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv 2016, https://doi.org/10.48550/arXiv.1506.01497.
• 16. H. Shijie, W. Zhonghao, S. Fuming, LEDet: A Single-Shot Real-Time Object Detector Based on Low-Light Image Enhancement, The Computer Journal 64(7) 2021 1028-1038, https://doi.org/10.1093/comjnl/bxab055.
• 17. A. Choudhury, R. Saha, S. Z. Shoumo, S. R. Tulon, J. Uddin, M. K. Rahman, An efficient way to represent braille using YOLO algorithm, Proceedings of the Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) (2018) 10-19, http://dx.doi.org/10.1109/ICIEV.2018.8641038.
• 18. D. Volivier, M. Saïd, A. M. Sidi, M. Pierre, L. Frédéric, A new Edge Architecture for AI-IoT services deployment, Proceedings of the 17th International Conference on Mobile Systems and Pervasive Computing (MobiSPC), (2020) 10-19, https://doi.org/10.1016/j.procs.2020.07.006.
• 19. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, Communications of the ACM 60(6) (2017) 84-90, https://doi.org/10.1145/3065386.
• 20. D. S. Viraktamath, P. Navalgi, A. Neelopant, Comparison of YOLOv3 and SSD Algorithms, Proceedings of the International Journal of Engineering Research & Technology (IJERT) (2021) 1156-1160.
• 21. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 779-788, http://dx.doi.org/10.1109/CVPR.2016.91.
• 22. Bochkovskiy, C. Y. Wang, H. Y. M. Liao, YOLOv4: Optimal speed and accuracy of object detection, arXiv 2020, https://doi.org/10.48550/arXiv.2004.10934.
• 23. Jiang, R. Luo, J. Mao, T. Xiao, Y. Jiang, Acquisition of Localization Confidence for Accurate Object Detection, In Proceedings of the European conference on computer vision (ECCV) (2018) 8-14, http://dx.doi.org/10.1007/978-3-030-01264-9_48.
• 24. Z. Zheng, P. Wang, D. Ren, W. Liu, R. Ye, Q. Hu, W. Zuo, Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation, arXiv 2021, https://doi.org/10.48550/arXiv.2005.03572.
• 25. K. K. Reddy, M. Shah, Recognizing 50 human action categories of web videos, Machine Vision and Applications 24 (2013) 971-981, https://doi.org/10.1007/s00138-012-0450-4

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).