scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

scholarly journals M-FCN based sea-surface weak target detection

2021 ◽  
Vol 32 (5) ◽  
pp. 1111-1118
Author(s):  
Pan Meiyan ◽  
Sun Jun ◽  
Yang Yuhao ◽  
Li Dasheng ◽  
Yu Junpeng
Keyword(s):  
Target Detection ◽  
Sea Surface ◽  
Weak Target

scholarly journals Study on Visual Detection Algorithm of Sea Surface Targets Based on Improved YOLOv3

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7263
Author(s):  
Tao Liu ◽  
Bo Pang ◽  
Shangmao Ai ◽  
Xiaoqiang Sun
Keyword(s):  
Target Detection ◽  
Visual Detection ◽  
Feature Fusion ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Sea Surface ◽  
Experimental Comparison ◽  
Feature Maps ◽  
The World ◽  
Marine Safety

Countries around the world have paid increasing attention to the issue of marine security, and sea target detection is a key task to ensure marine safety. Therefore, it is of great significance to propose an efficient and accurate sea-surface target detection algorithm. The anchor-setting method of the traditional YOLO v3 only uses the degree of overlap between the anchor and the ground-truth box as the standard. As a result, the information of some feature maps cannot be used, and the required accuracy of target detection is hard to achieve in a complex sea environment. Therefore, two new anchor-setting methods for the visual detection of sea targets were proposed in this paper: the average method and the select-all method. In addition, cross PANet, a feature fusion structure for cross-feature maps was developed and was used to obtain a better baseline cross YOLO v3, where different anchor-setting methods were combined with a focal loss for experimental comparison in the datasets of sea buoys and existing sea ships, SeaBuoys and SeaShips, respectively. The results showed that the method proposed in this paper could significantly improve the accuracy of YOLO v3 in detecting sea-surface targets, and the highest value of mAP in the two datasets is 98.37% and 90.58%, respectively.

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 Algorithm for Target Detection in Smart City Combined with Depth Learning and Feature Extraction

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Feng Wang ◽  
Zhiming Xu ◽  
Zemin Qiu ◽  
Weichuan Ni ◽  
Jiaqi Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Smart City ◽  
Feature Fusion ◽  
Smart Cities ◽  
Adaptive Strategy ◽  
Operating Conditions ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Depth Learning

The target detection algorithms have the problems of low detection accuracy and susceptibility to occlusion in existing smart cities. In response to this phenomenon, this paper presents an algorithm for target detection in a smart city combined with depth learning and feature extraction. It proposes an adaptive strategy is introduced to optimize the algorithm search windows based on the traditional SSD algorithm, which according to the target operating conditions change, strengthening the algorithm to enhance the accuracy of the objective function which is combined with the weighted correlation feature fusion method, and this method is a combination of appearance depth features and depth features. Experimental results show that this algorithm has a better antiblocking ability and detection accuracy compared with the conventional SSD algorithms. In addition, it has better stability in a changing environment.

scholarly journals Edge Intelligent Perception Method for Power Grid Icing Condition Based on Multi-Scale Feature Fusion Target Detection and Model Quantization

2021 ◽  
Vol 9 ◽  
Author(s):  
Fuqi Ma ◽  
Bo Wang ◽  
Min Li ◽  
Xuzhu Dong ◽  
Yifan Mao ◽  
...  
Keyword(s):  
Transmission Line ◽  
Target Detection ◽  
Power Transmission ◽  
Feature Fusion ◽  
Classical Method ◽  
Power Transmission Line ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Scale Feature ◽  
Multi Scale

Insulator is an important equipment of power transmission line. Insulator icing can seriously affect the stable operation of power transmission line. So insulator icing condition monitoring has great significance of the safety and stability of power system. Therefore, this paper proposes a lightweight intelligent recognition method of insulator icing thickness for front-end ice monitoring device. In this method, the residual network (ResNet) and feature pyramid network (FPN) are fused to construct a multi-scale feature extraction network framework, so that the shallow features and deep features are fused to reduce the information loss and improve the target detection accuracy. Then, the full convolution neural network (FCN) is used to classify and regress the iced insulator, so as to realize the high-precision identification of icing thickness. Finally, the proposed method is compressed by model quantization to reduce the size and parameters of the model for adapting the icing monitoring terminal with limited computing resources, and the performance of the method is verified and compared with other classical method on the edge intelligent chip.

Wideband Time-space Radon-Fourier Transform for High-speed and Weak Target Detection

2014 ◽  
Vol 35 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Li-chang Qian ◽  
Jia Xu ◽  
Wen-feng Sun ◽  
Ying-ning Peng
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
High Speed ◽  
Time Space ◽  
Weak Target
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