Lightweight Design for Infrared Dim and Small Target Detection in Complex Environments

被引:0
|
作者
Chang, Yan [1 ]
Ma, Decao [1 ]
Ding, Yao [1 ]
Chen, Kefu [1 ]
Zhou, Daming [2 ]
机构
[1] PLA Rocket Force Univ Engn, Xian 710025, Peoples R China
[2] Northwestern Polytech Univ, Sch Astronaut, Xian 710072, Peoples R China
关键词
object detection; lightweight; small infrared targets; attention mechanism; NETWORK; MODEL;
D O I
10.3390/rs16203761
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In the intricate and dynamic infrared imaging environment, the detection of infrared dim and small targets becomes notably challenging due to their feeble radiation intensity, intricate background noise, and high interference characteristics. To tackle this issue, this paper introduces a lightweight detection and recognition algorithm, named YOLOv5-IR, and further presents an even more lightweight version, YOLOv5-IRL. Firstly, a lightweight network structure incorporating spatial and channel attention mechanisms is proposed. Secondly, a detection head equipped with an attention mechanism is designed to intensify focus on small target information. Lastly, an adaptive weighted loss function is devised to improve detection performance for low-quality samples. Building upon these advancements, the network size can be further compressed to create the more lightweight YOLOv5-IRL version, which is better suited for deployment on resource-constrained mobile platforms. Experimental results on infrared dim and small target detection datasets with complex backgrounds indicate that, compared to the baseline model YOLOv5, the proposed YOLOv5-IR and YOLOv5-IRL detection algorithms reduce model parameter counts by 42.9% and 45.6%, shorten detection time by 13.6% and 16.9%, and enhance mAP0.5 by 2.4% and 1.8%, respectively. These findings demonstrate that the proposed algorithms effectively elevate detection efficiency, meeting future demands for infrared dim and small target detection.
引用
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页数:24
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