Method of Infrared Small Target Detection Based on Multi-depth Feature Connection
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 net-work.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 preser-vation 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.
Infrared small target detectionDeep learningObject detectionFeature connectionAttention mechanism
NETL
NSTL
万方数据
