A backbone-edge feature extraction method for varied industrial parts Online publication date: Tue, 14-Mar-2023
by Ping Wan; Ling Guo; Ming Li; Min Gao; Hongping Zhang; Jie Li
International Journal of System Control and Information Processing (IJSCIP), Vol. 4, No. 1, 2022
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.
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