| Citation: | TANG W T，LI B，JI M Q. Lightweight neural network design for infrared small ship detection[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2394-2403 （in Chinese） doi: 10.13700/j.bh.1001-5965.2024.0747
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A lightweight neural network design method is proposed to efficiently represent small ships in infrared remote sensing images. To improve the representation effect of infrared dim and small targets, a method for simulating the visual receptive field adjustment mechanism that incorporates multi-scale receptive field perception and selection processes is proposed. This method is inspired by the visual attention-driven receptive field adjustment mechanism. A lightweight feature selection operator is devised to enhance the receptive field selection, and feature reuse and convolution kernel decomposition are used to optimize the multi-scale receptive field perception process in order to further increase efficiency. Experimental results on an infrared dim and small ship detection dataset show that the network detection accuracy increased by 2%, with a reduction of 2.3×106 parameters and 9.1×109 computations compared to general lightweight networks. In complex scenarios with similar ground interference, this method effectively reduces false alarms and suppresses missed detections.
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