Abstract: Small infrared targets have the characteristics of a small number of pixels and a complex background,which leads to the problems of low detection accuracy and high time-consumption.This paper proposes a multi-depth feature connection network.Firstly,the model proposes a multi-depth cross-connect backbone to increase feature transfer between different layers and enhance feature extraction capabilities.Secondly,an attention-guided pyramid structure is designed to enhance the deep features and separate the background from the target.Thirdly,an asymmetric fusion decoding structure is proposed to enhance the preservation of texture information and position information in decoding.Finally,the model introduces point regression loss to get the center coordinates.The proposed network model is trained and tested on the SIRST dataset and the self-built infrared small target dataset.Experimental results show that compared with existing data-driven and model-driven algorithms,the proposed model has higher detection accuracy and faster speed in complex scenes.Compared with the suboptimal model,the average precision of the model is improved by 5.41%,and the detection speed reaches 100.8 FPS.

Key words: Infrared small target detection, Deep learning, Object detection, Feature connection, Attention mechanism