Automatic fishing net detection and recognition based on optical gated viewing for underwater obstacle avoidance

Xiaoquan Liu; Xinwei Wang; Pengdao Ren; Yinan Cao; Yan Zhou; Yuliang Liu

doi:10.1117/1.OE.56.8.083101

4 August 2017 Automatic fishing net detection and recognition based on optical gated viewing for underwater obstacle avoidance

Xiaoquan Liu, Xinwei Wang, Pengdao Ren, Yinan Cao, Yan Zhou, Yuliang Liu

Author Affiliations +

Optical Engineering, Vol. 56, Issue 8, 083101 (August 2017). https://doi.org/10.1117/1.OE.56.8.083101

Abstract

An automatic fishing net detection and recognition method for underwater obstacle avoidance is proposed. In the method, optical gated viewing technology is utilized to obtain high-resolution fishing net images and extend detection distance by suppressing water backscattering and background noise. The fishing net recognition is based on the proposed histograms of slope lines (HSLs) descriptors plus a support vector machine classifier. The extraction of HSL descriptors includes five steps of contrast-limited adaptive histogram equalization, the Gaussian low-pass filtering, the Canny detection, the Hough transform, and weighted vote. In the proof experiments, the detection distance of the fishing net reaches 5.7 attenuation length and the recognition accuracy reaches 93.79%.

Citation Download Citation

Xiaoquan Liu, Xinwei Wang, Pengdao Ren, Yinan Cao, Yan Zhou, and Yuliang Liu "Automatic fishing net detection and recognition based on optical gated viewing for underwater obstacle avoidance," Optical Engineering 56(8), 083101 (4 August 2017). https://doi.org/10.1117/1.OE.56.8.083101

Received: 13 March 2017; Accepted: 28 June 2017; Published: 4 August 2017

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