A Target Recognition Algorithm for Linear Array Compound Imaging Fuze under Jamming Conditions

doi:10.12382/bgxb.2024.0708

Abstract

Abstract:

In order to address the issue of the poor target recognition performance of laser imaging fuze under smoke,dust,and camouflage interference,a target recognition algorithm using linear array laser and linear array near-infrared compound imaging is proposed.A calibration matrix is established according to the imaging model,and the spatial mapping relationship between the laser point cloud and the near-infrared image is obtained.A deep learning-based target recognition algorithmic framework is constructed,and a voxel fusion module is proposed at the data input layer to enhance the point cloud by encoding near-infrared pixel-level features.A BEV fusion module is proposed at the middle layer to achieve feature-level fusion with adaptive dynamic adjustment of bimodal feature weights.The proposed algorithm is validated based on a custom simulation dataset.The experimental results show that the proposed algorithm can significantly improve the accuracy of target recognition under smoke,dust and camouflage interference.

Key words: compound imaging fuze, target recognition, laser point cloud, near-infrared image, fusion algorithm

GAO Mingze, XU Lixin, SHI Xiaolong, WANG Weihan, WANG Fengjie, HU Shiyuan, WU Yanjiang, CHEN Huimin. A Target Recognition Algorithm for Linear Array Compound Imaging Fuze under Jamming Conditions[J]. Acta Armamentarii, 2025, 46(7): 240708-.

Figures/Tables 10

References 25

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算法	模态	Δ	mAP	坦克AP@0.7			卡车AP@0.7			汽车AP@0.7
算法	模态	Δ	mAP	烟雾	扬尘	伪装	烟雾	扬尘	伪装	烟雾	扬尘	伪装
SECOND^[22]	L	-	33.85	64.20	70.38	68.97	29.45	21.41	34.97	5.39	8.67	1.20
PointPillar^[21]	L	+0.32	34.17	63.40	71.40	72.38	29.46	25.65	34.01	3.94	6.58	0.67
CLOCs^[17]	L+C	+4.11	38.28	79.41	80.50	78.99	35.50	18.18	35.93	8.08	3.47	4.44
MVX-Net^[12]	L+C	+48.21	86.49	85.75	80.70	82.23	83.61	86.09	81.05	97.08	87.59	94.29
BEVFusion^[20]	L+C	+5.19	91.68	99.73	99.90	90.91	87.88	87.39	89.96	86.90	85.13	97.30
本文算法	L+C	+4.07	95.75	90.13	98.87	98.71	90.06	98.48	98.66	99.10	89.18	98.57

算法	模态	Δ	mAP	坦克AP@0.7			卡车AP@0.7			汽车AP@0.7
算法	模态	Δ	mAP	烟雾	扬尘	伪装	烟雾	扬尘	伪装	烟雾	扬尘	伪装
SECOND^[22]	L	-	33.85	64.20	70.38	68.97	29.45	21.41	34.97	5.39	8.67	1.20
PointPillar^[21]	L	+0.32	34.17	63.40	71.40	72.38	29.46	25.65	34.01	3.94	6.58	0.67
CLOCs^[17]	L+C	+4.11	38.28	79.41	80.50	78.99	35.50	18.18	35.93	8.08	3.47	4.44
MVX-Net^[12]	L+C	+48.21	86.49	85.75	80.70	82.23	83.61	86.09	81.05	97.08	87.59	94.29
BEVFusion^[20]	L+C	+5.19	91.68	99.73	99.90	90.91	87.88	87.39	89.96	86.90	85.13	97.30
本文算法	L+C	+4.07	95.75	90.13	98.87	98.71	90.06	98.48	98.66	99.10	89.18	98.57

VFM	BEVFM	mAP
VFM	BEVFM	烟雾	扬尘	伪装
		95.39	92.91	90.83
√		93.82	92.09	93.84
	√	96.82	94.87	92.31
√	√	98.58	95.47	94.11

VFM	BEVFM	mAP
VFM	BEVFM	烟雾	扬尘	伪装
		95.39	92.91	90.83
√		93.82	92.09	93.84
	√	96.82	94.87	92.31
√	√	98.58	95.47	94.11