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Infrared Weak and Small Target Detection Algorithm Based on Deep Learning Cover

Infrared Weak and Small Target Detection Algorithm Based on Deep Learning

By: Lei Wang and  Jun Yu  
Open Access
|Sep 2024

Figures & Tables

Figure 1.

RT-DETR network structure
RT-DETR network structure

Figure 2

EMA module
EMA module

Figure 3.

CAMixing module
CAMixing module

Figure 4.

Image enhancement effect
Image enhancement effect

Figure 5.

AP change curve
AP change curve

Figure 6.

Improved detection results
Improved detection results

experimental environment

Experimental environmentVersion
CPUIntelCorei7-11800H
GPUNVIDIA GeForce RTX2080 Ti
LanguagePython3.8
Deep Learning FrameworkPytorch1.14.0
CUDA11.8.0

Ccomparison of algorithm enhancements

index algorithmPSNRSSIMEntropyAGEME
Original image 6.285041.91352.6388
SSR28.29700.855225.715044.26752.8967
MSR28.77720.86766.217644.91352.8932
DDE36.09890.96796.459444.22062.6857
Bilateral filtering34.66210.83956.315521.74071.4891
DDE+MSR28.75810.84366.279346.89692.8882

Comparative experiments of different parameters of shape-iou

s0.10.20.30.40.50.61.0
mAP(%)83.583.484.985.384.883.583.4

Compares the experimental results

EMACMAixingShape-IoUATFLP/%R/%AP/%Param/106
73.275.284.632.81
72.276.385.533.40
74.875.285.634.97
74.175.086.235.23
75.576.285.932.81
75.477.187.835.23
Language: English
Page range: 47 - 55
Published on: Sep 30, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Lei Wang, Jun Yu, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.