scholarly journals Radar small/mini target detection technology in strong clutter environment

2019 ◽  
Vol 2019 (20) ◽  
pp. 7130-7133
Author(s):  
Linyong Wu ◽  
Jin Mao ◽  
Weixiong Bai
Keyword(s):  
Target Detection ◽  
Detection Technology

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

Algorithm for Imaging Sonar Target Detection based on Geometry Characteristic and SVM

2013 ◽  
Vol 734-737 ◽  
pp. 3075-3078 ◽  
Author(s):  
Zhong Liu ◽  
Ke Li ◽  
Bing Yan
Keyword(s):  
Target Detection ◽  
Support Vector ◽  
Surveillance Systems ◽  
High Detection Rate ◽  
Detection Technology ◽  
High Detection ◽  
Underwater Target ◽  
Imaging Sonar ◽  
Geometry Characteristic ◽  
Classification Function

The attack from small speedboat and underwater robot is becoming more and more serious. And most powerful countries have installed underwater surveillance systems to protect port, ship, seashore depository and anchorage. The detection technology of underwater target is the most important technology in the process of surveillance. In order to take good advantage of the target’s geometry characteristic and classification function of SVM(Support Vector Machine), an algorithm for imaging sonar target detection based on geometry characteristic and SVM is proposed in the paper. The experiment results are presented and show that this algorithm is in high detection rate under complex underwater circumstance.

High resolution remote sensing image ship target detection technology based on deep learning

2019 ◽  
Vol 15 (5) ◽  
pp. 391-395 ◽  
Author(s):  
Min Wang ◽  
Jin-yong Chen ◽  
Gang Wang ◽  
Feng Gao ◽  
Kang Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Target Detection ◽  
Remote Sensing Image ◽  
Detection Technology
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