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

scholarly journals Image Recognition Technology in Texture Identification of Marine Sediment Sonar Image

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chao Sun ◽  
Li Wang ◽  
Nan Wang ◽  
Shaohua Jin
Keyword(s):  
Transfer Learning ◽  
Image Recognition ◽  
Marine Sediment ◽  
Original Model ◽  
Experimental Comparison ◽  
Texture Discrimination ◽  
Model Based ◽  
Sonar Image ◽  
Ocean Sediment ◽  
Sonar Images

Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP (average precision) value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images.

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 Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29416-29428
Author(s):  
Xiaoming Qin ◽  
Xiaowen Luo ◽  
Ziyin Wu ◽  
Jihong Shang
Keyword(s):  
Deep Learning ◽  
Side Scan Sonar ◽  
Sediment Classification ◽  
Sonar Images ◽  
Small Side

scholarly journals Improving SAR Automatic Target Recognition Models With Transfer Learning From Simulated Data

2017 ◽  
Vol 14 (9) ◽  
pp. 1484-1488 ◽  
Author(s):  
David Malmgren-Hansen ◽  
Anders Kusk ◽  
Jorgen Dall ◽  
Allan Aasbjerg Nielsen ◽  
Rasmus Engholm ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Target Recognition ◽  
Simulated Data ◽  
Automatic Target Recognition
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