Real-time infrared target detection based on center points

Zhuang MIAO; Yong ZHANG; Wei-Hua LI

doi:10.11972/j.issn.1001-9014.2021.06.021

Journals >Journal of Infrared and Millimeter Waves >Volume 40 >Issue 6 >Page 858 > Article

Journal of Infrared and Millimeter Waves
Vol. 40, Issue 6, 858 (2021)

Real-time infrared target detection based on center points

Zhuang MIAO^1、2, Yong ZHANG^1、*, and Wei-Hua LI^1、2

Author Affiliations

¹Key Laboratory of Infrared System Detection and Imaging Technology，Shanghai Institute of Technical Physics，Chinese Academy of Sciences，Shanghai 200083，China

²School of Electronic，Electrical，and Communication Engineering，University of Chinese Academy of Sciences，Beijing 100049，China

show less

DOI: 10.11972/j.issn.1001-9014.2021.06.021 Cite this Article

Zhuang MIAO, Yong ZHANG, Wei-Hua LI. Real-time infrared target detection based on center points[J]. Journal of Infrared and Millimeter Waves, 2021, 40(6): 858 Copy Citation Text

EndNote(RIS)

BibTex

Plain Text

show less

Fig. 1. The overall architecture of TCPD

Download full size | View in the Article

Fig. 2. The structure of blocks in FEM

Download full size | View in the Article

Fig. 3. The network structure of FFM

Download full size | View in the Article

Fig. 4. The network structure of BSM

Download full size | View in the Article

Fig. 5. The network structure of TPM

Download full size | View in the Article

Fig. 6. Examples on infrared dataset

Download full size | View in the Article

Fig. 7. Examples on the VOC dataset

Download full size | View in the Article

Stage	Output Size	Output Channels	Layer
Input	384×384	3	Image
Stage1	192×192	24	3×3，Conv，s2
Stage2	96×96	24	3×3，Max Pooling，s2
Stage3	48×48	116	Block1×1 Block2×4
Stage4	24×24	232	Block1×1 Block2×8
Stage5	12×12	464	Block1×1 Block2×4

Table 1. Network structure of FEM

View in the Article

Model	Input Size		mAP/（%）	AP /（%）
Model	Input Size		mAP/（%）	Bird	Fighter	Airliner	Helicopter	Trainer
CenterNet	384×384	88.04		76.73	88.95	94.91	90.77	88.84
YOLOv3	416×416	93.02		87.70	93.97	95.97	94.84	92.66
Tiny-YOLOv3	416×416	80.08		66.58	83.16	93.85	84.92	71.90
Tiny-YOLOv4	512×512	82.87		85.60	91.06	95.35	89.13	53.23
FKPD	384×384	88.98		79.40	90.84	95.01	90.27	89.39
TCPD	384×384	90.24		79.44	90.69	96.02	94.68	90.35

Table 2. Detection results on infrared dataset

View in the Article

Model	Input Size	mAP/（%）
CenterNet	384×384	68.24
YOLOv3	416×416	76.80
Tiny-YOLOv3	416×416	58.40
Tiny-YOLOv4	416×416	65.71
FKPD	384×384	61.61
TCPD	384×384	66.76

Table 3. Detection results on VOC dataset

View in the Article

Model	FLOPs/Bn	Parameters/M	Inference Time/ms
CenterNet	8.69	14.22	48.90
YOLOv3	27.93	61.63	134.07
Tiny-YOLOv3	2.34	8.68	31.71
Tiny-YOLOv4	2.91	5.88	26.23
FKPD	1.55	2.03	25.86
TCPD	0.49	0.95	21.69

Table 4. Real-time analysis of TCPD

View in the Article

Model	Input Size	mAP/（%）	Inference Time/ms
TCPD（baseline）	384×384	90.24	21.69
TCPD-small	320×320	89.85	18.22
TCPD-large	512×512	92.38	32.70
TCPD-compressed	384×384	88.60	17.90
TCPD w/o FFM	384×384	89.29	20.15
TCPD w/o BSM	384×384	88.75	20.43

Table 5. Ablation study on the design of model

View in the Article

$α$	Dataset （mAP/（%））
$α$	Infrared	VOC
0.35	89.42	64.81
0.55	90.56	66.00
0.75	90.24	66.76
0.95	90.31	66.24

Table 6. Ablation study of Gaussian kernel

Zhuang MIAO, Yong ZHANG, Wei-Hua LI. Real-time infrared target detection based on center points[J]. Journal of Infrared and Millimeter Waves, 2021, 40(6): 858

Download Citation

Save the article for my favorites

Paper Information