Ship detection with lightweight network based on YOLOV3

Decheng Kong; Ping Wang; Xiang Wei; Zeyu Xu

doi:10.1117/12.2640800

20 September 2022 Ship detection with lightweight network based on YOLOV3

Decheng Kong, Ping Wang, Xiang Wei, Zeyu Xu

Proceedings Volume 12261, International Conference on Mechanical Design and Simulation (MDS 2022); 1226141 (2022) https://doi.org/10.1117/12.2640800
Event: Second International Conference on Mechanical Design and Simulation (MDS 2022), 2022, Wuhan, China

Abstract

This paper proposes a lightweight network for ship detection based on YOLOV3. This method reduces the parameters and model size of YOLOV3 while retaining accuracy from three aspects. First, an efficient way is described of replacing the Darknet-53 of YOLOV3 with a reduced network. Second, an optimization form is demonstrated to improve the basic unit. Last, compression and acceleration strategies are implemented in the method. The performance of our method and YOLOV3 is measured on a self-built dataset for ship detection. The method we proposed is 29× less parameters, 85× less size, and 3× less inference time than YOLOV3. In the experiment, the method is deployed on a Qihang Unmanned Surface Vehicle (USV) and realizes real-time detection of ships.

Citation Download Citation

Decheng Kong, Ping Wang, Xiang Wei, and Zeyu Xu "Ship detection with lightweight network based on YOLOV3", Proc. SPIE 12261, International Conference on Mechanical Design and Simulation (MDS 2022), 1226141 (20 September 2022); https://doi.org/10.1117/12.2640800

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KEYWORDS

Convolution

Quantization

Performance modeling

Environmental sensing

Electrical engineering

Feature extraction

Neural networks

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