A Lightweight Infrared Small Target Detection Network Based on Target Multiscale Context
Tianlei Ma, Zhen Yang, Benxue Liu, Siyuan Sun
Abstract
To solve the problems of slow detection speed and poor robustness of existing infrared (IR) small target detection methods in complex environments, a lightweight detection model MiniIR-net is proposed in this letter. In the MiniIR-net model, to reduce the number of parameters required for model fitting, a multiscale target context feature extraction (TCVE) module is proposed to enrich the feature expression of the target. In addition, to improve the feature mapping capability of MiniIR-net, a feature mapping upsampling network by fusing the deep and shallow features is designed. In the process of feature mapping upsampling, the network uses target features with different depths to make up for the loss of target features caused by pooling. It is proven by the experiment that the proposed MiniIR-net network is superior to the existing detection methods in detection speed, accuracy and robustness in a complex environment. The model size of MiniIR-net is at least 1/260 of the current detection model, and the detection accuracy is improved by at least 5%. The source code of this article can be obtained at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yangzhen1252/MiniIR-net</uri> .