基于全局感知机制的地面红外目标检测方法

doi:10.12305/j.issn.1001-506X.2022.05.05

Abstract

Abstract:

Aiming at the problems of infrared target detection in ground scenes, such as complex background interference, low detection accuracy, false detection and missed detection, an infrared target detection method based on global perception mechanism is proposed. Based on Darknet-53 as the backbone network, combined with the spatial pyramid pooling mechanism with global information fusion, the global information perception and anti-interference ability of the model are enhanced while increasing the sensing domain of the model. The smooth focus loss function is designed to solve the problems of low detection accuracy, false detection and missed detection caused by the interaction of targets in the image. Experiments show that on the infrared-voc320 data set, the average detection accuracy of the algorithm is 80.1%, which is 4.4% higher than that of YOLOv3, and the detection speed reaches 56.4 FPS, which effectively improves the detection accuracy of infrared targets under complex background and realizes the real-time detection of infrared targets.

Key words: infrared target detection, YOLOv3, deep learning, loss function, spatial pyramid pooling

CLC Number:

TP391.4

Xiaofeng ZHAO, Yebin XU, Fei WU, Jiahui NIU, Wei CAI, Zhili ZHANG. Ground infrared target detection method based on global sensing mechanism[J]. Systems Engineering and Electronics, 2022, 44(5): 1461-1467.

Figures/Tables 9

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References 30

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模型	准确率/ %	帧数/ (Frame·s^－1)	平均精度/ @0.5/%
Faster R-CNN	80.3	7	75.6
SSD300	76.5	44	70.4
YOLOv3	80.28	58.8	75.7
Infrared-YOLO	82.7	56.4	80.1

基准模型	S-FL	全局感知池化金字塔机制	精度@0.5/%		平均精度@0.5/%
基准模型	S-FL	全局感知池化金字塔机制	小汽车	公交车	平均精度@0.5/%
√	-	-	77.8	73.6	75.7
√	√	-	78.1	77.3	77.7
√	-	√	79.0	78.5	78.8
√	√	√	81.4	78.9	80.1

基准模型	空间金字塔机制	全局感知空间金字塔机制	空间复杂度	参数/Mb	平均精度@0.5/%
√	-	-	155.1	58.67	75.7
√	√	-	155.9	59.68	76.5
√	-	√	189.1	46.71	78.8

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Ground infrared target detection method based on global sensing mechanism

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