Deep learning for sea cucumber detection using stochastic gradient descent algorithm. (22nd June 2020)

• Record Type: Journal Article
• Authors: Zhang, Huaqiang; Yu, Fusheng; Sun, Jincheng; Shen, Xiaoqin; Li, Kun
• Is Part Of: European journal of remote sensing. Volume 53(2020)Supplement 1
• Page Start: 53
• Page End: 62
• Publication Date: 2020-06-22
• Subjects: Sea cucumber -- deep learning (DL) -- stochastic gradient descent algorithm -- underwater sea cucumber identification -- C-Watch; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2020.1715265
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: A large number of natural products secluded from sea atmosphere has been identified for the pharmacodynamic probable in varied illness handlings, such as, tumor or inflammatory states. Sea cucumber culturing and fishing is mainly reliant on physical works. For quick and precise programmed recognition, deep residual networks with various forms used to recognize the submarine sea cucumber. The imageries have been taken by a C-Watch distantly worked submarine automobile. To improve the pixel quality of the image, a training algorithm called Stochastic Gradient Descent algorithm (SGD) has been proposed in this paper. It explains how efficiently fetching the picture characteristics to expand the accurateness of sea cucumber detection, that might be reached by higher training information set and preprocessing information set with remove and denoising procedures towards increase picture eminence. Furthermore, the DL network might be linked through faster expertise to settle the location, also recognize the number of sea cucumber inimages, and weightiness valuation modeling is similarly required to be progressed to execute programmed take actions. The functioning of the planned technique specifies excellent latent for manual sea cucumber detection..