Sage Journals: Discover world-class research

Abstract

In response to the challenges of underwater garbage recognition due to insufficient lighting, poor visibility, and complex background interference in underwater environments, this paper proposes an improved YOLOv5s-based underwater garbage recognition algorithm. By integrating the CBAM(Convolutional Block Attention Module) attention mechanism into the C3 structure of YOLOv5s, the ability to capture subtle features of underwater debris has been significantly enhanced. Additionally, GhostConv lightweight convolution layers have been introduced into the model’s neck network, which not only accelerates the computational speed but also ensures the stability of feature extraction. Experiments show that the proposed algorithm achieves a recognition accuracy of 88.0% on a self-built underwater garbage dataset with an average detection time of just 6.8 ms. This improved model surpasses both YOLOv5s and similar algorithms in recognition accuracy and operational efficiency. This research not only greatly enhances the precision and real-time performance of underwater garbage monitoring but also provides an effective solution for the automated monitoring of underwater environments.

Keywords

underwater garbage recognition CBAM attention mechanism lightweight convolution layer YOLOv5

Get full access to this article

View all access options for this article.

References

Chen

Fang

Zheng

, et al. Microplastic pollution in wild commercial nekton from the South China Sea and Indian Ocean, and its implication to human health. Mar Environ Res 2021; 167: 105295. DOI: 10.1016/j.marenvres.2021.105295.

Shih

Chen

, et al. Towards underwater sustainability using ROV equipped with deep learning system. In: 2020 International Automatic Control Conference (CACS), Hsinchu, Taiwan, 4–7 November 2020, 2020. DOI: 10.1109/CACS50047.2020.9289788.

Xue

Huang

Wei

, et al. An efficient deep-sea debris detection method using deep neural networks. IEEE J Sel Top Appl Earth Obs Rem Sens 2021; 14: 12348–12360.

Girshick

Donahue

Darrell

, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, USA, 23-28 June 2014.

Ren

Girshick

, et al. Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 2015: 28.

Zhang

Ren

, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Machine Intell 2015; 37(9): 1904–1916.

Redmon

Divvala

Girshick

, et al. You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788.

Jiang

Wang

. Underwater object detection based on improved single shot MultiBox detector. In: Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China, December 24-26 2020.

Liu

Anguelov

Erhan

, et al. SSD: Single Shot MultiBox Detector. CoRR 2015, abs/1512.02325.

10.

Fulton

Hong

Islam

, et al.

Robotic detection of marine litter using deep visual detection models

2018. DOI: 10.48550/arXiv.1804.01079.

11.

Lin

Zhong

Liu

, et al.

RoIMix: proposal-fusion among multiple images for underwater object detection

2019. DOI: 10.48550/arXiv.1911.03029.

12.

Liu

Song

Ding

. WQT and DG-YOLO: towards domain generalization in underwater object detection 2020.

13.

Wei

, et al. MLDet: towards efficient and accurate deep learning method for Marine Litter Detection. Ocean Coast Manag 2023; 243: 106765. DOI: 10.1016/j.ocecoaman.2023.106765.

14.

Liu

Wang

, et al.

YOLOX: exceeding YOLO series in 2021

2021. DOI: 10.48550/arXiv.2107.08430.

An improved YOLOv5 algorithm for underwater garbage recognition

Abstract

Keywords

Get full access to this article

References