Volume 38, Number 4, August 2020
|Page(s)||747 - 754|
|Published online||06 October 2020|
Exploring Underwater Target Detection Algorithm Based on Improved SSD
School of Marine engineering, Northwestern Polytechnical University, Xi'an 710072, China
2 Xi'an Horological Research Institute or Light Industry Corporation Ltd., Xi'an 710061, China
3 Institute of Acoustic, Chinese Academic of Sciences, Beijing 100190, China
4 Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Beijing 100190, China
As the in-depth exploration of oceans continues, the accurate and rapid detection of fish, bionics and other intelligent bodies in an underwater environment is more and more important for improving an underwater defense system. Because of the low accuracy and poor real-time performance of target detection in the complex underwater environment, we propose a target detection algorithm based on the improved SSD. We use the ResNet convolution neural network instead of the VGG convolution neural network of the SSD as the basic network for target detection. In the basic network, the depthwise-separated deformable convolution module proposed in this paper is used to extract the features of an underwater target so as to improve the target detection accuracy and speed in the complex underwater environment. It mainly fuses the depthwise separable convolution when the deformable convolution acquires the offset of a convolution core, thus reducing the number of parameters and achieving the purposes of increasing the speed of the convolution neural network and enhancing its robustness through sparse representation. The experimental results show that, compared with the SSD detection model that uses the ResNet convolution neural network as the basic network, the improved SSD detection model that uses the depthwise-separated deformable convolution module improves the accuracy of underwater target detection by 11 percentage points and reduces the detection time by 3 ms, thus validating the effectiveness of the algorithm proposed in the paper.
Key words: underwater target detection / SSD / depthwise-separated convolution / deformable convolutional
关键字 : 水下目标检测 / SSD / 深度可分离卷积 / 可变形卷积
© 2020 Journal of Northwestern Polytechnical University. All rights reserved.
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