Ship detection in infrared images based on YOLOV4 and salient regions

Panpan Zhang; Haibo Luo; Zheng Xu; Miao He

doi:10.1117/12.2616841

15 February 2022 Ship detection in infrared images based on YOLOV4 and salient regions

Panpan Zhang, Haibo Luo, Zheng Xu, Miao He

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121663N (2022) https://doi.org/10.1117/12.2616841
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Infrared image ship detection has important applications in military and civil affairs. Because infrared images are not easy to acquire in large quantities, deep neural networks cannot directly use infrared images for training; if the pre-trained model of visible light images is directly used for detection, the phenomenon of missed detection will be caused due to different imaging conditions. In response to this problem, this paper proposes a detection method that combines a deep convolutional neural network and salient region. Firstly, we proposed a method extracting salient region based on anchor and saliency map, then multiple new images are formed by salient regions, and the newly formed images and the original image are input to the deep convolutional neural network for parallel processing, and finally the results of the detection are integrated to produce the final detection results by the non-maximum suppression (NMS) method. The comparison results show that the method proposed in this paper can effectively reduce the rate of missed detection and thus improve the accuracy of detection.

Citation Download Citation

Panpan Zhang, Haibo Luo, Zheng Xu, and Miao He "Ship detection in infrared images based on YOLOV4 and salient regions", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121663N (15 February 2022); https://doi.org/10.1117/12.2616841

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