DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model

Abstract

A new deep learning method based on the target gray-level distribution constraint mechanism model is proposed to solve the infrared dim small target detection problem in the complex environment. First, to solve the uneven distribution of positive and negative samples, the designed smoothness operator is used to suppress the background and enhancement target by measuring the difference in their features in 1D and 2D gradient. Second, an infrared dim small target detection network based on dense feature fusion, namely the DFFIR-net network, is proposed. The DFFIR-net enhances the feature expression of dim small targets by integrating the original features and the smoothness features of gray-level gradient. Also, the DFFIR-net alleviates the problem of sparse feature extraction. Finally, a multiscale 2D Gaussian label generation strategy is proposed. This strategy is critical in supervising the training of DFFIR-net in multi-dimensional Gaussian space, improving the feature exploration ability of the network and detection performance under small training samples. The experimental results show that compared with the existing advanced detection methods, the proposed method has higher accuracy and lower false alarm rates in various complex scenes.