Abstract
This paper aims to evaluate the state-of-the-art object detection network; YOLOv5s (You Only Look Once version 5 small) for the detection of underwater marine debris using AUVs. The development of machine learning and AUVs for detecting marine debris is reviewed. In the paper, the YOLOv5s model is trained on a marine debris dataset using transfer learning. Several other object detection models are also trained on the same dataset for comparison. The results of the trained models are evaluated and the YOLOv5s model is deployed on an Android device to determine its suitability for real-time marine debris detection onboard AUVs. Overall, the YOLOv5s was able to achieve high accuracy scores of up to 91.2% and fast detection speeds of up to 20FPS on a Poco X3 Pro.
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References
Canals, M., Pham, C.K., Bergmann, M., et al.: The quest for seafloor macrolitter: A critical review of background knowledge, current methods, and future prospects. Environm. Res. Lett. (2020)
Zacchini, L., Ridolfi, A., Topini, A., et al.: Deep learning for on-board auv automatic target recognition for optical and acoustic imagery. IFAC-PapersOnLine 53, 14589–14594 (2020)
Naddaf-Sh, M., Myler, H., Zargarzadeh, H.: Design and implementation of an assistive real-time red lionfish detection system for AUV/ROVs. Complexity 2018, 1–10 (2018)
Wang, C.C., Samani, H.: Object Detection using Transfer Learning for Underwater Robot. In: International Conference on Advanced Robotics and Intelligent Systems (ARIS), pp. 1–4 (2020)
Song, Y., He, N., Liu, P.: Real-time object detection for AUVs using self-cascaded convolutional neural networks. IEE J. Oceanic Eng. 46(1) (2021)
Flores, H., Zuniga, A., Motlagh, N.H., et al.: PENGUIN: aquatic plastic pollution sensing using AUVs. In: Proceedings of the 6th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications (DroNet 2020), Article 5, pp. 1–6. Association for Computing Machinery, New York (2020)
Fulton, M., Hong, J.S., Islam, M.J., et al.: Robotic detection of marine litter using deep visual detection models.In: 2019 International Conference on Robotics and Automation (2019)
Matias, V.: Submerged marine debris detection with autonomous underwater vehicles. In: 2016 International Conference on Robotics and Automation for Humanitarian Applications (2016)
Majchrowska, S., Mikolajczyk, A., Ferlin, M., et al.: Deep learning-based waste detection in natural and urban environments. Waste Manag. 138 (2022)
Xue, B., Huang, B.X., Wei, W.B., et al.: An efficient deep-sea debris detection method using deep neural networks. IEEE J. Selected Topics Appli. Earth Observat. Remote Sens. 14, 12348–12360 (2021)
Watanabe, J., Shao, Y., Miura, N.: Underwater and airborne monitoring of marine ecosystems and debris. J. Appli. Remote Sens. 13(4) (2019)
Politikos, D.V., Fakiris, E., Davvetas, A., et al.: Automatic detection of seafloor marine litter using towed camera images and deep learning. Mar. Pollut. Bull. 164, 111974 (2021)
Singh, D., Matias, V.: The marine debris dataset for forward-looking sonar semantic segmentation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (2021)
Kikaki, K., Kakogeorgiou, I., Mikeli, P., et al.: MARIDA: a benchmark for marine debris detection from sentinel-2 Remote Sensing Data. PLOS ONE 1(1) (2022)
Wolf, M., van der Berg, K., Garaba, S.P., et al.: Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q). Environ. Res. Lett. 15(11), 114042 (2020)
Hong, J.S., Fulton, M., Sattar, J.: Trashcan: A Semantically-Segmented Dataset Towards Visual Detection of Marine Debris”. arXiv: 2007.08097 (2020)
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Chia, K.Y., Chin, C.S., See, S. (2023). Deep Transfer Learning Application for Intelligent Marine Debris Detection. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_39
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DOI: https://doi.org/10.1007/978-3-031-34204-2_39
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