On Sonar Image Processing Techniques for Detection and Localization of Underwater Objects

Weng, Li; Li, Min; Gong, Zhen Bang

doi:10.4028/www.scientific.net/amm.236-237.509

Cited by 5 publications

(2 citation statements)

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“…The detection results in term of PA, MA, MIU, and FWIU by Eqs. (6) to (9) are listed in Tab.1. From Fig.13 and Tab.1, it can be seen that the performance of MS-SegNet is the best, and its PA is over 95%, SegNet is better than U-Net and FCN.…”

Section: Detection Resultsmentioning

confidence: 99%

“…Underwater object detection algorithm divide into a supervised segmentation method and unsupervised segmentation method. Supervised segmentation methods include Bayesian framework and variation theory framework [6], [7]. The Bayesian framework uses the similarity between local pixel statistics and seabed prototype statistics to represent the conditional likelihood function [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sonar Image Detection Based on Multi-Scale Multi-Column Convolution Neural Networks

Wang¹,

Zhang²

2019

IEEE Access

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Automatic detection of underwater objects by sonar images is an important and challenging topic in applications of Autonomous Underwater Vehicle (AUV) under the complex marine environment. A detection method is proposed based on Multi-Scale Multi-Column Convolution Neural Networks (MSMC-CNNs). Firstly, the Multi-Scale Multi-Column CNNs is used to form an encoder network for extracting multi-scale features of the sonar image. Secondly, the bicubic linear interpolation algorithm is used as the deconvolution process of the decoder networks to restore the sonar image size and resolution. Moreover, a novel transfer learning manner based on progressive fine-tuning to accelerate the model training. Finally, the proposed method is validated on the sonar image dataset and is compared with other existing detection methods. The pixel accuracy (PA) of MSMC-CNNs for different categories sonar image is over 95%. The experiment results show that the MSMC-CNNs model has better detection effect and more robustness to noise. INDEX TERMS Underwater object detection, MSMC-CNNs, bicubic linear interpolation algorithm, deconvolution. He received the B.S. degree in mathematics from Northwest University, China, in 1988, the M.S. degree in applied mathematics from Northwest Polytechnic University, China, in 1995, and the Ph.D. degree in electromagnetic field and microwave from Air Force Engineering University, China, in 2001. He is currently a Professor with Xijing University and a Visiting Scholar with the Department of Computer Science, Virginia Tech. His research interests include machine learning and its application in data mining, including machine learning, leaf image processing, data reduction, data mining, feature selection, wavelet transforms, and their application in the sonar image recognition.

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Section: Detection Resultsmentioning

confidence: 99%