An automatic target detection algorithm based on wavelet analysis for infrared image small target in background of sea and sky

2003 ◽  
Author(s):  
Ying Wei ◽  
Zelin Shi ◽  
Haibin Yu
Keyword(s):  
Wavelet Analysis ◽  
Target Detection ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Small Target ◽  
Automatic Target Detection

Infrared dim small target detection algorithm based on NSCT and SVD

2015 ◽  
Author(s):  
Ying Zhao ◽  
Gang Liu ◽  
Huixin Zhou ◽  
Hanlin Qin ◽  
Xiao Li ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

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