Research on Methods of Micro-target Detection in Various Application Scenarios

Abstract

This paper provides a comprehensive introduction and comparison of the research on small target detection algorithms in different application scenarios and systematically explores the application of deep learning algorithms for small target detection in remote sensing imagery, visible imagery, and infrared imagery. In remote sensing images, the Deconvolution R-CNN algorithm is used to improve the accuracy of small target detection by adding a deconvolution layer. In the field of visible images, a Gaussian Mixture Model (GMM)-based approach is introduced to achieve fast detection of small targets with SIFT features and a GMM target detector. In infrared images, a comprehensive algorithm adapted to low signal-to-noise ratio and complex background is proposed to automatically detect tiny targets through background suppression and threshold selection optimization. In this paper, we compare and analyze the performance of these methods in different scenarios, and propose directions for future research, including expanding the training dataset, applying super-resolution methods to improve the image resolution, and enhancing the universality of the algorithms. This paper provides a comprehensive overview of research in the field of small target detection, as well as an outlook on future research directions, which provides a useful reference for the further development of the field.