Ma, J.; Guo, H.; Rong, S.; Feng, J.; He, B. Infrared Dim and Small Target Detection Based on Background Prediction. Remote Sens.2023, 15, 3749.
Ma, J.; Guo, H.; Rong, S.; Feng, J.; He, B. Infrared Dim and Small Target Detection Based on Background Prediction. Remote Sens. 2023, 15, 3749.
Ma, J.; Guo, H.; Rong, S.; Feng, J.; He, B. Infrared Dim and Small Target Detection Based on Background Prediction. Remote Sens.2023, 15, 3749.
Ma, J.; Guo, H.; Rong, S.; Feng, J.; He, B. Infrared Dim and Small Target Detection Based on Background Prediction. Remote Sens. 2023, 15, 3749.
Abstract
Infrared dim and small target detection is a key technology of various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ingore global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method is proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction is applied for detecting targets. The framework contains of coarse detection module and fine detection module. In coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, for further optimize the result, inpainting is utilized to predict the background by the global semnantic of images. In this paper, inpainting algorithm with a mask aware dynamic filtering module is incorporated into the fine detection stage to estimate background in candidate target areas. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework have effective detection capability and robustness for complex surroundings.
Keywords
Dim and small target detection; background prediction; image inpainting; Region Proposal Network (RPN)
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
