Vision-based underwater target real-time detection for autonomous underwater vehicle subsea exploration

Frontiers in Marine Science

Marine Science

Gaofei Xu¹  Wei Guo¹  Libiao Yuan²  Daoxian Zhou²  Yinlong Zhang²  Zepeng Huang³ 

[1] Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, China;State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China;Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China;Underwater Archaeology Department, National Center for Archaeology, Beijing, China;

关键词: autonomous underwater vehicle;    subsea exploration;    real-time target detection;    lightweight convolutional neural network;    underwater image enhancement;   

DOI  :  10.3389/fmars.2023.1112310

 received in 2022-11-30, accepted in 2023-05-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

Autonomous underwater vehicles (AUVs) equipped with online visual inspection systems can detect underwater targets during underwater operations, which is of great significance to subsea exploration. However, the undersea scene has some instinctive challenging problems, such as poor lighting conditions, sediment burial, and marine biofouling mimicry, which makes it difficult for traditional target detection algorithms to achieve online, reliable, and accurate detection of underwater targets. To solve the above issues, this paper proposes a real-time object detection algorithm for underwater targets based on a lightweight convolutional neural network model. To improve the imaging quality of underwater images, contrast limited adaptive histogram equalization with the fused multicolor space (FCLAHE) model is designed to enhance the image quality of underwater targets. Afterwards, a spindle-shaped backbone network is designed. The inverted residual block and group convolutions are used to extract depth features to ensure the target detection accuracy on one hand and to reduce the model parameter volume on the other hand under complex scenarios. Through extensive experiments, the precision, recall, and mAP of the proposed algorithm reached 91.2%, 90.1%, and 88.3%, respectively. It is also noticeable that the proposed method has been integrated into the embedded GPU platform and deployed in the AUV system in the practical scenarios. The average computational time is 0.053s, which satisfies the requirements of real-time object detection.

【 授权许可】

Unknown   
Copyright © 2023 Xu, Zhou, Yuan, Guo, Huang and Zhang