scholarly journals A New Radiometric Correction Method for Side-Scan Sonar Images in Consideration of Seabed Sediment Variation

2017 ◽  
Vol 9 (6) ◽  
pp. 575 ◽  
Author(s):  
Jianhu Zhao ◽  
Jun Yan ◽  
Hongmei Zhang ◽  
Junxia Meng
Keyword(s):  
Correction Method ◽  
Radiometric Correction ◽  
Seabed Sediment ◽  
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.

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

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 RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1985
Author(s):  
Qi Wang ◽  
Meihan Wu ◽  
Fei Yu ◽  
Chen Feng ◽  
Kaige Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Semantic Segmentation ◽  
Processing Unit ◽  
Real Time Processing ◽  
Side Scan Sonar ◽  
Network Training ◽  
Sonar Sensor ◽  
Sonar Images

Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

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