A Novel Infrared Small Target Detection Algorithm Based on Deep Learning

2020 ◽  
Author(s):  
Xin Xu ◽  
Yawen Sun ◽  
Lianghui Ding ◽  
Feng Yang
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

Infrared small target detection algorithm based on feature salience

2011 ◽  
Vol 98 (1) ◽  
pp. 137-145 ◽  
Author(s):  
Zhen-Xue Chen ◽  
Cheng-Yun Liu ◽  
Fa-Liang Chang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

Motion estimation and spatial-temporal filter-based infrared small target detection algorithm

2015 ◽  
Vol 8 (3) ◽  
pp. 256 ◽  
Author(s):  
Zhonghua Wang ◽  
Yuan Liao ◽  
Qingping Liu ◽  
Chunyong Li
Keyword(s):  
Motion Estimation ◽  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection ◽  
Temporal Filter ◽  
Infrared Small Target

A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm

2018 ◽  
Vol 89 ◽  
pp. 387-397 ◽  
Author(s):  
Saed Moradi ◽  
Payman Moallem ◽  
Mohamad Farzan Sabahi
Keyword(s):  
False Alarm ◽  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection ◽  
Multi Scale ◽  
Infrared Small Target

scholarly journals An infrared small target detection algorithm based on high-speed local contrast method

2016 ◽  
Vol 76 ◽  
pp. 474-481 ◽  
Author(s):  
Zheng Cui ◽  
Jingli Yang ◽  
Shouda Jiang ◽  
Junbao Li
Keyword(s):  
Target Detection ◽  
High Speed ◽  
Detection Algorithm ◽  
Local Contrast ◽  
Small Target ◽  
Small Target Detection ◽  
Contrast Method ◽  
Infrared Small Target

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON SELF-ADAPTIVE BACKGROUND FORECAST

2007 ◽  
Vol 27 (12) ◽  
pp. 1619-1624 ◽  
Author(s):  
Zhenxue Chen ◽  
Guoyou Wang ◽  
Jianguo Liu ◽  
Chengyun Liu
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection ◽  
Background Forecast ◽  
Infrared Small Target ◽  
Self Adaptive

scholarly journals A Robust Infrared Small Target Detection Algorithm Based on Human Visual System

2014 ◽  
Vol 11 (12) ◽  
pp. 2168-2172 ◽  
Author(s):  
Jinhui Han ◽  
Yong Ma ◽  
Bo Zhou ◽  
Fan Fan ◽  
Kun Liang ◽  
...  
Keyword(s):  
Visual System ◽  
Target Detection ◽  
Human Visual System ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

Infrared small target detection algorithm based on potential regions proposal

2019 ◽  
Author(s):  
Shuaihao Wang ◽  
Jiangpeng Du ◽  
Juanfang Chai ◽  
Yiji Liu ◽  
Chengshi Tang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

scholarly journals Infrared Small Target Detection via Modified Random Walks

2018 ◽  
Vol 10 (12) ◽  
pp. 2004 ◽  
Author(s):  
Chaoqun Xia ◽  
Xiaorun Li ◽  
Liaoying Zhao
Keyword(s):  
Random Walks ◽  
Target Detection ◽  
Detection Algorithm ◽  
Local Contrast ◽  
Small Target ◽  
Global Uniqueness ◽  
Dimensional Image ◽  
Small Target Detection ◽  
Small Targets ◽  
Infrared Small Target

Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in both target enhancement and detection performance.

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