Enhanced YOLO v3 Tiny Network for Real-Time Ship Detection From Visual Image

IEEE Access

Jianbo Liu¹  Cheng Yang¹  Zhaoquan Gu²  Hao Li³  Lianbing Deng³ 

[1] College of Information Engineering, Communication University of China, Beijing, China;Cyberspace Institute of Advanced Technology (CIAT), Guangzhou University, Guangzhou, China;Da Hengqin Science and Technology Development Company Ltd., Zhuhai, China;

关键词: Object detection;    ship detection;    convolutional neural network;    model tuning;    attention module;   

DOI  :  10.1109/ACCESS.2021.3053956

来源: DOAJ

【 摘 要 】

Different from ship detection from synthetic aperture radar (SDSAR) and ship detection from spaceborne optical images (SDSOI), ship detection from visual image (SDVI) has better detection accuracy and real-time performance, which can be widely used in port management, cross-border ship detection, autonomous ship, safe navigation, and other real-time applications. In this paper, we proposed a new SDVI algorithm, named enhanced YOLO v3 tiny network for real-time ship detection. The algorithm can be used in video surveillance to realize the accurate classification and positioning of six types of ships (including ore carrier, bulk cargo carrier, general cargo ship, container ship, fishing boat, and passenger ship) in real-time. Based on the original YOLO v3 tiny network, we have made the following fine tunings. 1) The preset anchors trained on Seaship annotation data have the similar “dumpy” shape as the normal ships, helping the network to achieve faster and better training; 2) Convolution layer instead of max-pooling layer and expanding the channels of prediction network improve the small target detection ability of the algorithm. 3) Due to the problem that large-scale ships are easily disturbed by the onshore building, complex waves and light on the water surface, we introduced attention module named CBAM into the backbone network, which make the model more focused on the target. The detection accuracy of the proposed algorism is obviously better than that of the original YOLO v3 tiny work. Although it is slightly inferior to the Yolo v3 network, it has faster speed than Yolo v3. However, the proposed algorithm is a better trade-off between real-time performance and detection accuracy, and is more suitable for actual scenes. Compared with the SOAT algorithm in Z. Shao et al. (2020), our algorithm has a 9.6% improvement in mAP and a faster speed.

【 授权许可】

Unknown