Underwater target's size/shape dynamic analysis for fast target recognition using sonar images

2002 ◽  
Author(s):  
Yingchun Lu ◽  
Enfang Sang
Keyword(s):  
Dynamic Analysis ◽  
Target Recognition ◽  
Sonar Images

scholarly journals Sonar image recognition of underwater target based on convolutional neural network

2021 ◽  
Vol 39 (2) ◽  
pp. 285-291
Author(s):  
Leilei Jin ◽  
Hong LIANG ◽  
Changsheng Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Target Recognition ◽  
Saliency Detection ◽  
Image Features ◽  
Feature Maps ◽  
Sonar Image ◽  
Sonar Images ◽  
Underwater Target

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

scholarly journals Real-Time Underwater Maritime Object Detection in Side-Scan Sonar Images Based on Transformer-YOLOv5

2021 ◽  
Vol 13 (18) ◽  
pp. 3555
Author(s):  
Yongcan Yu ◽  
Jianhu Zhao ◽  
Quanhua Gong ◽  
Chao Huang ◽  
Gen Zheng ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Target Localization ◽  
Average Precision ◽  
Image Preprocessing ◽  
Side Scan Sonar ◽  
Sonar Images ◽  
Underwater Target

To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image.

scholarly journals Shipwreck Target Recognition in Side-Scan Sonar Images by Improved YOLOv3 Model Based on Transfer Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 173450-173460
Author(s):  
Tang Yulin ◽  
Shaohua Jin ◽  
Gang Bian ◽  
Yonghou Zhang
Keyword(s):  
Transfer Learning ◽  
Target Recognition ◽  
Side Scan Sonar ◽  
Model Based ◽  
Sonar Images
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