Underwater Object Detection Method with Enhanced Wavelet Transform Features

WEI Nan; YANG Wankou; ZHOU Weijie; JIANG Longyu

doi:10.11993/j.issn.2096-3920.2025-0003

Volume 33 Issue 2

May 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(2): 204-211

WEI Nan, YANG Wankou, ZHOU Weijie, JIANG Longyu. Underwater Object Detection Method with Enhanced Wavelet Transform Features[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 204-211. doi: 10.11993/j.issn.2096-3920.2025-0003

Citation:

WEI Nan, YANG Wankou, ZHOU Weijie, JIANG Longyu. Underwater Object Detection Method with Enhanced Wavelet Transform Features[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 204-211. doi: 10.11993/j.issn.2096-3920.2025-0003

Citation:

PDF( 816 KB)

Underwater Object Detection Method with Enhanced Wavelet Transform Features

doi: 10.11993/j.issn.2096-3920.2025-0003

1.
College of Software Engineering, Southeast University, Nanjing 211102, China
2.
School of Automation, Southeast University, Nanjing 211102, China
3.
School of Computer Science and Engineering, Southeast University, Nanjing 211102, China

Received Date: 2025-01-06
Accepted Date: 2025-02-25
Rev Recd Date: 2025-02-18

Available Online: 2025-03-24

Abstract

Abstract

The complex and unique underwater environment results in low-quality underwater images, characterized by low contrast, blurriness, and underwater degradation, which significantly affects the capabilities of underwater object detection. To address this issue, this paper proposed an underwater object detection method with enhanced wavelet transform features. The paper introduced discrete wavelet transform(DWT) to decompose the multi-level features extracted by the deep learning framework into high- and low-frequency components. These frequency domain feature components were then interactively enhanced using a frequency domain interaction module based on the attention mechanism designed in this work, optimizing the ability of feature expression. The enhanced features were subsequently fed into the object detection network to improve the object detection performance. Experimental results demonstrate that the proposed underwater object detection method outperforms conventional object detection methods, significantly improving the ability to detect objects in underwater environments.
- underwater object detection,
- deep learning,
- wavelet transform

FullText(HTML)

References(23)

References

[1]	FU C P, LIU R S, FAN X, et al. Rethinking general underwater object detection: Datasets, challenges, and solutions[J]. Neurocomputing, 2023, 517: 243-256. doi: 10.1016/j.neucom.2022.10.039
[2]	LIN W H, ZHONG J X, LIU S, et al. Roimix: Proposal-fusion among multiple images for underwater object detection[C]//ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE, 2020: 2588-2592.
[3]	CHEN L, LIU Z, TONG L, et al. Underwater object detection using invert multi-class adaboost with deep learning[C]//2020 International Joint Conference on Neural Networks(IJCNN). Glasgow, United Kingdom: IEEE, 2020: 1-8.
[4]	LIANG X, SONG P. Excavating ROI attention for underwater object detection[C]//2022 IEEE International Conference on Image Processing(ICIP). Bordeaux, France: IEEE, 2022: 2651-2655.
[5]	SONG P, LI P, DAI L, et al. Boosting R-CNN: Reweighting R-CNN samples by RPN's error for underwater object detection[J]. Neurocomputing, 2023, 530: 150-164.
[6]	FU C, FAN X, XIAO J, et al. Learning heavily-degraded prior for underwater object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(11): 6887-6896.
[7]	ZHOU J, HE Z, LAM K M, et al. AMSP-UOD: When vortex convolution and stochastic perturbation meet underwater object detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Vancouver, Canada: AAAI, 2024, 38(7): 7659-7667.
[8]	HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA: IEEE, 2016: 770-778.
[9]	SUN S, REN W, WANG T, et al. Rethinking image restoration for object detection[J]. Advances in Neural Information Processing Systems, 2022, 35: 4461-4474.
[10]	ALFRED H. Zur theorie der orthogonalen funktionensysteme[J]. Mathematische Annalen, 1910, 69: 331-371.
[11]	LIU C, LI H, WANG S, et al. A dataset and benchmark of underwater object detection for robot picking[C]//2021 IEEE International Conference on Multimedia & Wxpo workshops(ICMEW). Shenzhen, China: IEEE, 2021: 1-6.
[12]	PEDERSEN M , HAURUM J B , GADE R, et al. Detection of marine animals in a new underwater dataset with varying visibility[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach, California, USA: IEEE, 2019: 18-26.
[13]	DENG J, DONG W, SOCHER R, et al. Imagenet: A large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Florida, USA: IEEE, 2009: 248-255.
[14]	TIAN Z, SHEN C, CHEN H, et al. FCOS: A simple and strong anchor-free object detector[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 44(4): 1922-1933.
[15]	REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(6): 1137-1149.
[16]	YANG Z, LIU S, HU H, et al. Reppoints: Point set representation for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, Korea: IEEE, 2019: 9657-9666.
[17]	LI X, WANG W, WU L, et al. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection[J]. Advances in Neural Information Processing Systems, 2020, 33: 21002-21012.
[18]	ZHU C, HE Y, SAVVIDES M. Feature selective anchor-free module for single-shot object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, California, USA: IEEE, 2019: 840-849.
[19]	ZHANG S, CHI C, YAO Y, et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, Washington, USA: IEEE, 2020: 9759-9768.
[20]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017: 2980-2988.
[21]	SUN P, ZHANG R, JIANG Y, et al. Sparse R-CNN: End-to-end object detection with learnable proposals[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Nashville, Tennessee, USA: IEEE, 2021: 14454-14463.
[22]	CAI Z, VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Utah, USA: IEEE, 2018: 6154-6162.
[23]	JIANG L, WANG Y, JIA Q, et al. Underwater species detection using channel sharpening attention[C]//Proceedings of the 29th ACM International Conference on Multimedia. Chengdu, China: ACM Multimedia, 2021: 4259-4267.