Small target detection in sonar images with multilevel feature screening and task dynamic alignment

Wang Yan; Wang Honghui; Liu Shudong; Zhang Yan; Hao Zeyu

doi:10.12086/oee.2024.240196

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Wang Y, Wang H H, Liu S D, et al. Small target detection in sonar images with multilevel feature screening and task dynamic alignment[J]. Opto-Electron Eng, 2024, 51(10): 240196. doi: 10.12086/oee.2024.240196

Citation:

Wang Y, Wang H H, Liu S D, et al. Small target detection in sonar images with multilevel feature screening and task dynamic alignment[J]. Opto-Electron Eng, 2024, 51(10): 240196. doi: 10.12086/oee.2024.240196

Small target detection in sonar images with multilevel feature screening and task dynamic alignment

School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300000, China

Fund Project: Project supported by Tianjin Philosophy and Social Science Planning Project (TJGL19xSX-045)

More Information

^*Corresponding author: liushudong@tcu.edu.cn
CSTR: 32245.14.oee.2024.240196

Received Date 20 August 2024

Revised Date 21 September 2024

Accepted Date 21 September 2024

Published Date 25 October 2024

Abstract

Abstract

To solve the problem of small target detection in sonar images, which is difficult, low precision, and prone to misdetection and omission detection, this paper proposes an improved algorithm for small target detection in sonar images based on YOLOv8s. Firstly, considering that small targets in sonar images usually have low contrast and are easily overwhelmed by noise, an efficient multi-level screening feature pyramid network (EMS-FPN) is proposed. Secondly, since the classification branch and localization branch of the decoupled head are independent, which will increase the number of parameters of the model, and at the same time, it is difficult to effectively adapt to the detection needs of targets of different scales, resulting in poor detection of small targets, the task dynamic alignment detection head module (TDADH) is designed. Finally, to verify the effectiveness of the model in this paper, the corresponding validation was carried out on URPC2021 and SCTD expanded sonar dataset, mAP0.5 improved by 0.3% and 1.8% compared with YOLOv8s, respectively, and the number of parameters was reduced by 22.5%. The results show that the method proposed in this paper not only improves the accuracy but also significantly reduces the number of model parameters in the task of target detection in sonar images.
- underwater target detection /
- sonar image /
- small target detection /
- efficient multilevel screening /
- task dynamic alignment /
- lightweighting

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References

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Overview

Overview

Sonar technology has an important application value in the marine field, and is widely used in seabed geological exploration, marine environmental pollution monitoring, underwater target detection, marine resources development, and other fields. However, the detection of small targets in sonar images has always been a challenging problem due to the fact that sonar imaging is affected by a variety of factors, such as the marine environment and underwater target characteristics. Small targets, such as round cages and balls, often face difficulties such as weak signals, complex backgrounds, low resolution, and noise interference in sonar images, and their effective detection is crucial to ensure the safety of underwater navigation and the development of marine resources. To solve the problem of small target detection in sonar images, which is difficult, low precision, and prone to wrong detection and leakage, a lightweight sonar image small target detection algorithm based on YOLOv8s with efficient multilevel feature fusion is proposed. Firstly, considering that small targets in sonar images usually have low contrast and are easily overwhelmed by noise, an efficient multilevel screening feature fusion pyramid EMS-FPN module is proposed. It can highlight the important features through the screening mechanism, suppress irrelevant background noise, and extracting features from different scales achieve multilevel fusion so as to improve the detection capability of small targets. Secondly, since the classification branch and the localization branch of the decoupling head are independent, it will increase the number of parameters of the model and lead to the problem of lack of interaction between the two tasks. It is difficult to effectively adapt to the detection needs of targets at different scales, resulting in poor detection of small targets, therefore, the task dynamic align detection head (TDADH) module is designed to learn the task interaction features from multiple convolutional layers through a feature extractor to obtain joint features to effectively adapt to the detection needs of targets at different scales, and finally, to validate the effectiveness of the model in this paper, corresponding validation is carried out on the URPC2021 and SCTD sonar datasets, and the detection accuracy mAP50 is improved compared with that of YOLOv8s respectively by 0.3% and 1.8%, and the number of parameters is reduced by 22.5%. The results show that the sonar image target detection algorithm proposed in this paper improves the accuracy and significantly reduces the number of model parameters.