文献类型：期刊文献

英文题名：A Circular Target Feature Detection Framework Based on DCNN for Industrial Applications

作者：Tan, Dayu[1];Chen, Linggang[2];Jiang, Chao[1];Zhong, Weimin[1];Du, Wenli[1];Qian, Feng[1];Mahalec, Vladimir[3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Anhui Univ Sci & Technol, Sch Comp Sci & Technol, Huainan 232001, Peoples R China;[3]McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L7, Canada

年份：2021

卷号：17

期号：5

起止页码：3303

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20210910013400);WOS:【SCI-EXPANDED(收录号:WOS:000622100800030)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61925305, Grant 61890930-3, and Grant 61988101 and in part by the International (Regional) Cooperation and Exchange Project under Grant 61720106008.

语种：英文

外文关键词：Feature extraction; Target recognition; Object detection; Informatics; Transforms; Support vector machines; Training; Catalyst particles; deep convolutional neural network (DCNN); Hough transform; industrial circles; target detection; threshold processing

摘要：This article presents a novel target detection method, which is named as circular target feature detection framework based on a deep convolutional neural network (DCNN). The central proposition of this method uses the optimized DCNN architecture to detect the target and locate the position of the circle accurately in the image field of view. In this article, a Hough transform based on threshold processing (HTP) is embedded into the optimized DCNN architecture, which calculates the center positions and radius of all circles by training the circular samples for each detected rectangular frame. It can efficiently identify small circular target materials in the industry and screen out unqualified particles. The experimental results show that the boundary information of the circles is obtained clearly from the complex noise background images, thereby accurately determining the location of the circle. It has some advantages over only using a specific circular recognition algorithm. We proposed the new study on HTP-DCNN, which has extremely high accuracy in the field of machine vision positioning with circles for industrial applications.