International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 16, No. 1, 8 Feb. 2026
Cover page and Table of Contents: PDF (size: 1139KB)
Anchor-Free Yolov8 for Robust Underwater Debris Detection and Classification
PDF (1139KB), PP.19-28
Views: 0 Downloads: 0
Author(s)
Index Terms
Underwater Debris Detection and Classification, Deep Convolution Neural Network, Autonomous Underwater Vehicles, JAMSTEC Dataset
Abstract
Deep-sea debris poses a significant threat to marine life and human health. Traditional methods for underwater debris detection and classification are labour-intensive and inefficient. The major challenge for using vision robots or autonomous underwater vehicles(AUVs) to remove deep sea debris is to exactly identify the marine debris. Marine debris gets deformed, eroded, and blocked due to seawater. Marine debris changes its shape, size, and texture in sea environment. Sea environment is challenging for the task of debris detection because of weak light. Uncertainty about the task of debris detection is due to marine life, rocks, marine flora, fauna, algae, etc. This study aims to develop a robust deep learning model for underwater debris detection and classification using YOLOV8. We evaluate the performance of YOLOV8 against YOLOV3 and YOLOV5 on the JAMSTEC TrashCan dataset. By employing an anchor-free detection head, YOLOV8 demonstrates improved accuracy in detecting underwater debris of varying shapes, sizes, and textures. Here, we show that YOLOV8 achieves a mean Average Precision (mAP) of 0.5095, outperforming YOLOV3 (mAP: 0.31879) and YOLOV5 (mAP: 0.43608). Our findings underscore the potential of anchor-free YOLOV8 in addressing the challenges of underwater debris detection, which is crucial for marine conservation efforts.
Cite This Paper
Sheetal A. Takale, "Anchor-Free Yolov8 for Robust Underwater Debris Detection and Classification", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.1, pp. 19-28, 2026. DOI:10.5815/ijem.2026.01.02
Reference
[1]K. Sasaki, T. Sekine, L.-J. Burtz, and W. J. Emery, “Coastal marine debris detection and density mapping with very high resolution satellite imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 6391–6401, 2022.
[2]C. Nakkach and K. Moussi, “Deep learning and augmented reality based pollution detection system for sea surface applications,” in 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science (BCD), 2023, pp. 303–307.
[3]T. M. Kamal and B. D. N, “Accurate detection of aquatic debris in ocean surfaces using alexnet algorithm over resnet-50 algorithm,” in 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), 2023, pp. 292–295.
[4]A. Kankane and D. Kang, “Detection of seashore debris with fixed camera images using computer vision and deep learning,” in 2021 6th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), vol. 6, 2021, pp. 34–38.
[5]A. A. Alsuwaylimi, “Enhanced yolov8-seg instance segmentation for real-time submerged debris detection,” IEEE Access, vol. 12, pp. 117833–117849, 2024.
[6]F. Zocco, T.-C. Lin, C.-I. Huang, H.-C. Wang, M. O. Khyam, and M. Van, “Towards more efficient efficientdets and real-time marine debris detection,” IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 2134–2141, 2023.
[7]J. Music, S. Kru´ ziˇ c, I. Stan´ ciˇ c, and F. Alexandrou, “Detecting underwater sea litter using deep neural networks: An´ initial study,” in 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), 2020, pp. 1–6.
[8]K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv e-prints, Sep. 2014.
[9]K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
[10]M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520.
[11]R. Bajaj, S. Garg, N. Kulkarni, and R. Raut, “Sea debris detection using deep learning: Diving deep into the sea,” in 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), 2021, pp. 1–6.
[12]S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.
[13]W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C. Fu, and A. C. Berg, “SSD: single shot multibox detector,” CoRR, vol. abs/1512.02325, 2015. [Online]. Available: http://arxiv.org/abs/1512.02325
[14]J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” CoRR, vol. abs/1804.02767, 2018. [Online]. Available: http://arxiv.org/abs/1804.02767
[15]A. Bochkovskiy, C. Wang, and H. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” CoRR, vol. abs/2004.10934, 2020.
[16]J. Liu and Y. Zhou, “Marine debris detection model based on the improved yolov5,” in 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE), 2023, pp. 725–728.
[17]M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 10778–10787.
[18]B. Xue, B. Huang, W. Wei, G. Chen, H. Li, N. Zhao, and H. Zhang, “An efficient deep-sea debris detection method using deep neural networks,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 12348–12360, 2021.
[19]“Deep-sea debris database.” [Online]. Available: https://www.godac.jamstec.go.jp/dsdebris/e/index.html
[20]B. Xue, B. Huang, G. Chen, H. Li, and W. Wei, “Deep-sea debris identification using deep convolutional neural networks,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8909– 8921, 2021.
[21]K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” CoRR, vol. abs/1603.05027, 2016.
[22]A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” CoRR, vol. abs/1704.04861, 2017.
[23]Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
[24]W. Zhou, F. Zheng, G. Yin, Y. Pang, and J. Yi, “Yolotrashcan: A deep learning marine debris detection network,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–12, 2023.
[25]J. Hong, M. Fulton, and J. Sattar, “Trashcan: A semantically-segmented dataset towards visual detection of marine debris,” CoRR, vol. abs/2007.08097, 2020.