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

被引:1
|
作者
Xu, Gaofei [1 ]
Zhou, Daoxian [2 ,3 ,4 ]
Yuan, Libiao [2 ,3 ,4 ]
Guo, Wei [1 ]
Huang, Zepeng [5 ]
Zhang, Yinlong [2 ,3 ,4 ]
机构
[1] Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang, Peoples R China
[3] Chinese Acad Sci, Key Lab Networked Control Syst, Shenyang, Peoples R China
[4] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang, Peoples R China
[5] Natl Ctr Archaeol, Underwater Archaeol Dept, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
autonomous underwater vehicle; subsea exploration; real-time target detection; lightweight convolutional neural network; underwater image enhancement;
D O I
10.3389/fmars.2023.1112310
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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.
引用
收藏
页数:12
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