4 August 2017 Automatic fishing net detection and recognition based on optical gated viewing for underwater obstacle avoidance
Author Affiliations +
Optical Engineering, 56(8), 083101 (2017). doi: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%.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Xiaoquan Liu, Xinwei Wang, Pengdao Ren, Yinan Cao, Yan Zhou, 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). http://dx.doi.org/10.1117/1.OE.56.8.083101 Submission: Received 13 March 2017; Accepted 28 June 2017
Submission: Received 13 March 2017; Accepted 28 June 2017
JOURNAL ARTICLE
7 PAGES


SHARE
Back to Top