面向红外弱小舰船检测的轻量化神经网络设计

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.