Salient target detection method under sea surface environment based on multi-scale phase spectrum

2011 ◽  
Author(s):  
Lei Ren ◽  
Chaojian Shi ◽  
Xin Ran
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Phase Spectrum ◽  
Sea Surface ◽  
Multi Scale ◽  
Surface Environment

scholarly journals IYOLO: Multi-scale infrared target detection method based on bidirectional feature fusion

2021 ◽  
Vol 1873 (1) ◽  
pp. 012020
Author(s):  
Xiaofeng Zhao ◽  
Yebin Xu ◽  
Fei Wu ◽  
Wei Cai ◽  
Zhili Zhang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Multi Scale

scholarly journals Improved YOLOv4 Marine Target Detection Combined with CBAM

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 623
Author(s):  
Huixuan Fu ◽  
Guoqing Song ◽  
Yuchao Wang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Feature Learning ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Detection Technology ◽  
Time Requirements ◽  
Small Targets ◽  
Sea Area

Marine target detection technology plays an important role in sea surface monitoring, sea area management, ship collision avoidance, and other fields. Traditional marine target detection algorithms cannot meet the requirements of accuracy and speed. This article uses the advantages of deep learning in big data feature learning to propose the YOLOv4 marine target detection method fused with a convolutional attention module. Marine target detection datasets were collected and produced and marine targets were divided into ten categories, including speedboat, warship, passenger ship, cargo ship, sailboat, tugboat, and kayak. Aiming at the problem of insufficient detection accuracy of YOLOv4’s self-built marine target dataset, a convolutional attention module is added to the YOLOv4 network to increase the weight of useful features while suppressing the weight of invalid features to improve detection accuracy. The experimental results show that the improved YOLOv4 has higher detection accuracy than the original YOLOv4, and has better detection results for small targets, multiple targets, and overlapping targets. The detection speed meets the real-time requirements, verifying the effectiveness of the improved algorithm.

scholarly journals Small and Dim Ship Target Detection Based on Sea-Sky-Line

2019 ◽  
Vol 37 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Yaohui Hu ◽  
Ke Zhang ◽  
Chao Xing
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Sea Surface ◽  
Line Detection ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Noise Point ◽  
The Mean ◽  
Potential Area

In order to solve the problem of small and dim ship target detection under complex sea-sky background, we propose a target detection algorithm based on sea-sky line detection. Firstly, the paper locates the sea-sky-line based on fully convolutional networks, through which target potential area can be determined and disturbance can be excluded. Then the method based on the mean of four detection gradient is adopted to detect the small and dim ship target. The simulation results show that the method of sea-sky-line detection based on fully convolutional networks can overcome the disadvantages of the traditional methods and is suitable for complex background. The detection method proposed can filter the white noise point on the sea surface and thus can reduce false alarm, through which the detection of small and dim ship can be completed well.

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