Research on Infrared Target Detection and Tracking in Dark Environments

doi:10.12382/bgxb.2024.0081

Abstract

Abstract:

The efficient and accurate detection and tracking of dynamic target features in dark environments,where contours are blurred and occlusions are present,hold practical significance for disaster relief,search and tracking operations.To effectively detect and track the blurred contour features in dark environments,an improved real-time infrared target tracking and detection algorithm is proposed.This algorithm,based on the deep learning network (Spatial Local Dynamic You Only Look Once,SLD-YOLOv8),incorporates a non-local adaptive module and a spatial channel convolution (SCC) correlation module to optimize the Bottleneck CSP of YOLOv8 network for better feature extraction.A dedicated 160×160 detection layer and a dynamic head are introduced for the improved detection of small-scale targets and the enhanced boundary regression capabilities in low-light scenarios,enabling the accurate real-time inference of relative target position.Experimental validation shows that the proposed algorithm has good robustness and accuracy in detecting the dynamic features in dark environments.The average precision evaluation metrics mAP_0.5 and mAP_0.5:0.95 of this model are increased by 5.6% and 4.5%,respectively,compared to the original model,affirming its effectiveness of tracking the targets in dark environments.

Key words: dark environments, deep learning, attention mechanism, target detection and tracking, non-local, dynamic head

LIU Hui, LI Mingyi, HAN Lijin, LIU Baoshuai. Research on Infrared Target Detection and Tracking in Dark Environments[J]. Acta Armamentarii, 2025, 46(8): 240081-.

Figures/Tables 17

References 23

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真实情况	预测情况
真实情况	正例	反例
正例	TP(真正例)	FN(假反例)
反例	FP(假正例)	TN(真反例)

真实情况	预测情况
真实情况	正例	反例
正例	TP(真正例)	FN(假反例)
反例	FP(假正例)	TN(真反例)

模型				P/%	R/%	mAP_0.5/%	推理时间/ms
B	SC	NL	Dy	P/%	R/%	mAP_0.5/%	推理时间/ms
P P	P			80.4	74.6	78.6	7.6
P P	P			82.6	77.8	82.5	7.8
P		P		81.8	75.3	78.5	9.4
P			P	81.7	76.4	79.6	6.8
P	P	P		83.7	79.5	82.4	8.7
P	P	P	P	85.3	80.7	83.7	7.5

模型				P/%	R/%	mAP_0.5/%	推理时间/ms
B	SC	NL	Dy	P/%	R/%	mAP_0.5/%	推理时间/ms
P P	P			80.4	74.6	78.6	7.6
P P	P			82.6	77.8	82.5	7.8
P		P		81.8	75.3	78.5	9.4
P			P	81.7	76.4	79.6	6.8
P	P	P		83.7	79.5	82.4	8.7
P	P	P	P	85.3	80.7	83.7	7.5

方法	主干网络	mAP_0.5/%		mAP_0.5:0.95/%		推理时间/ms
方法	主干网络	D1	D2	D1	D2	D1	D2
DETR^[20]	ResNet-101	76.4	80.4	56.4	62.2	17.6	42.4
Deformable DETR^[21]	ResNet-101	77.3	78.6	56.3	61.8	15.8	40.6
FasterR-CNN^[22]	ResNet-101	72.8	74.5	50.9	59.6	23.3	38.2
ATSS^[23]	ResNet-101	76.9	78.2	56.9	60.5	16.4	46.3
YOLOv8n	CSPDarknet53	78.9	81.7	58.1	62.8	8.3	21.9
YOLOv8s	CSPDarknet53	79.4	82.2	59.4	64.7	9.1	24.6
YOLOv8m	CSPDarknet53	80.3	84.4	60.3	66.3	9.5	32.7
YOLOv8l	CSPDarknet53	83.4	85.3	61.2	67.2	12.8	36.8
YOLOv8x	CSPDarknet53	85.7	88.3	63.8	68.7	17.4	39.6
SLD-YOLOv8n	Modified CSP	84.5	85.3	62.6	66.8	7.8	15.2
SLD-YOLOv8s	Modified CSP	84.9	85.9	62.7	67.5	8.4	16.8
SLD-YOLOv8m	Modified CSP	85.4	87.4	63.7	68.1	8.6	19.4
SLD-YOLOv8l	Modified CSP	88.3	89.6	65.4	70.4	10.7	23.7
SLD-YOLOv8x	Modified CSP	91.1	92.7	70.8	71.3	15.4	30.2