Infrared image-based remote target detection for maritime rescue utilizing a deep learning network and data augmentation

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In this paper, a fast and robust infrared remote target detection network is proposed based on deep learning. Furthermore, we construct our own IR image database imitating humans in remote maritime rescue situations using FLIR M232 IR camera. First, IR image is preprocessed with contrast enhancement for data augmentation and to increase Signal-to-Noise Ratio (SNR). Second, multi-scale feature extraction is performed combined with fixed weighted kernels and convolutional neural network layers. Lastly, the feature map is mapped into a likelihood map indicating the potential locations of the targets. Experimental results reveal that the proposed method can detect remote targets even under complex backgrounds surpassing the previous methods by a significant margin of +0.62 in terms of mIOU.
Publisher
SPIE
Issue Date
2023-02-01
Language
English
Citation

AI and Optical Data Sciences IV

DOI
10.1117/12.2649806
URI
http://hdl.handle.net/10203/306723
Appears in Collection
ME-Conference Papers(학술회의논문)
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