IMD-Net: Interpretable multi-scale detection network for infrared dim and small objects
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Published:2024
Issue:1
Volume:21
Page:1712-1737
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Li Dawei1, Lin Suzhen2, Lu Xiaofei3, Zhang Xingwang1, Cui Chenhui2, Yang Boran2
Affiliation:
1. College of Electricity and Control Engineering, North University of China, Taiyuan 030051, China 2. College of Data Science and Technology, North University of China, Taiyuan 030051, China 3. Jiuquan Satellite Launch Center, Dongfeng Frame, Jiuquan 735000, China
Abstract
<abstract>
<p>This study proposed an interpretable multi-scale infrared small object detection network (IMD-Net) design method to improve the precision of infrared small object detection and contour segmentation in complex backgrounds. To this end, a multi-scale object enhancement module was constructed, which converted artificially designed features into network structures. The network structure was used to enhance actual objects and extract shallow detail and deep semantic features of images. Next, a global object response, channel attention, and multilayer feature fusion modules were introduced, combining context and channel information and aggregated information, selected data, and decoded objects. Finally, the multiple loss constraint module was constructed, which effectively constrained the network output using multiple losses and solved the problems of high false alarms and high missed detections. Experimental results showed that the proposed network model outperformed local energy factor (LEF), self-regularized weighted sparse model (SRWS), asymmetric contextual modulation (ACM), and other state of the art methods in the intersection-over-union (IoU) and <italic>F<sub>measure</sub></italic> values by 10.8% and 11.3%, respectively. The proposed method performed best on the currently available datasets, achieving accurate detection and effective segmentation of dim and small objects in various infrared complex background images.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
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