Title: A backbone-edge feature extraction method for varied industrial parts

Authors: Ping Wan; Ling Guo; Ming Li; Min Gao; Hongping Zhang; Jie Li

Addresses: Logistics Academy, Chongqing, 401331, China ' Logistics Academy, Chongqing, 401331, China ' Logistics Academy, Chongqing, 401331, China ' Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China ' Logistics Academy, Chongqing, 401331, China ' Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331 China

Abstract: Empty-loading ratio (ELR) in manufacturing factories is challenged due to plenty of industrial parts with diverse sizes and shapes, where edge detection is a primary operation to obtain ELR. Unlike traditional edge detection tasks, the purpose of ELR is to gain backbones of industrial parts and filter the details. Therefore, we present a backbone-edge feature extraction approach to deal with ELR computation problem. There into, a multi-scale block CNN model is structured to learn primary information of industrial parts through hybrid operations (i.e., horizontal and vertical combinations) with both deep and shallow features. In the model, a neighbour-information-based loss function is designed to enhance backbone information. Furthermore, a ELR value is obtained through discovering minimised closed-regions based on backbone edge information from our model. Simulations on industrial parts in conveyor boxes from real world indicate that the proposed approach outperforms other state-of-the-art methods.

Keywords: edge detection; backbone feature; neighbour information; CNN; convolutional neural networks; industrial parts; pixel neighbour; feature combination; adhesion processing; multiple scale; edge details; edge enhancement.

DOI: 10.1504/IJSCIP.2022.129562

International Journal of System Control and Information Processing, 2022 Vol.4 No.1, pp.28 - 42

Received: 09 Jul 2021
Accepted: 17 Mar 2022
Published online: 14 Mar 2023 *

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