scholarly journals Learning Objectness from Sonar Images for Class-Independent Object Detection

Author(s):  
Matias Valdenegro-Toro
Author(s):  
Ruijie Chang ◽  
Yaomin Wang ◽  
Jiaru Hou ◽  
Shuqi Qiu ◽  
Rui Nian ◽  
...  

2021 ◽  
Vol 13 (18) ◽  
pp. 3555
Author(s):  
Yongcan Yu ◽  
Jianhu Zhao ◽  
Quanhua Gong ◽  
Chao Huang ◽  
Gen Zheng ◽  
...  

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.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 102540-102553
Author(s):  
Longyu Jiang ◽  
Tao Cai ◽  
Qixiang Ma ◽  
Fanjin Xu ◽  
Shijie Wang

2019 ◽  
Vol 52 (21) ◽  
pp. 152-155 ◽  
Author(s):  
Sejin Lee ◽  
Byungjae Park ◽  
Ayoung Kim

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1180 ◽  
Author(s):  
Divas Karimanzira ◽  
Helge Renkewitz ◽  
David Shea ◽  
Jan Albiez

The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems along the underwater object detection pipeline. A pipeline for realizing a robust generic object detector will be described and demonstrated on a case study of detection of an underwater docking station in sonar images. The system shows an overall detection and classification performance average precision (AP) score of 0.98392 for a test set of 5000 underwater sonar frames.


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