Intelligent Detection and Recognition of Seabed Targets in Side-Scan Sonar Images

2020 ◽  
pp. 249-275
Author(s):  
Ziyin Wu ◽  
Fanlin Yang ◽  
Yong Tang
Keyword(s):  
Side Scan Sonar ◽  
Intelligent Detection ◽  
Sonar Images ◽  
Detection And Recognition

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

Underwater Sonar Image Segmentation Based on Snake Model

2013 ◽  
Vol 448-453 ◽  
pp. 3675-3678
Author(s):  
Jun Peng Wu ◽  
Hai Tao Guo
Keyword(s):  
Image Segmentation ◽  
Greedy Algorithm ◽  
Echo Signal ◽  
Constraint Forces ◽  
Shape Accuracy ◽  
Snake Model ◽  
Sonar Image ◽  
Sonar Images ◽  
Underwater Target ◽  
Detection And Recognition

The underwater sonar image segmentation has been a topic of research for decades. Underwater sonar image is based on the interaction by the echo signal of sound toward the underwater objects or targets. Because of the serious noises polution and the dim target edge, the contrast and resolution of sonar images are obtaind in a decreased quanlity. This paper proposes an improved snake model that focuses on solving underwater target detection and recognition. According to the traditional snake model, it is defined as an energy minimizing spline which is influenced by external constraint forces, and it can guide the image forces to pull toward features, such as lines or edges. Compared with the traditional snake model, this snake model greedy algorithm can converge to the contours more quickly and more stably, especially in complex underwater environments. Examination of the results shows that using snake model greedy algorithm has a more clear shape accuracy.

Deblurring of GLORIA side-scan sonar images

1992 ◽  
Vol 14 (2) ◽  
pp. 125-136 ◽  
Author(s):  
D. C. Mason ◽  
T. P. LeBas ◽  
I. Sewell ◽  
C. Angelikaki
Keyword(s):  
Side Scan Sonar ◽  
Sonar Images

scholarly journals A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex

2019 ◽  
Vol 11 (11) ◽  
pp. 1281 ◽  
Author(s):  
Xiufen Ye ◽  
Haibo Yang ◽  
Chuanlong Li ◽  
Yunpeng Jia ◽  
Peng Li
Keyword(s):  
Constant Coefficient ◽  
Correction Method ◽  
Time Varying ◽  
Gray Level ◽  
Original Image ◽  
Gray Scale ◽  
Side Scan Sonar ◽  
Retinex Theory ◽  
Sonar Images ◽  
Beam Patterns

When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document