Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

doi:10.3390/app11209473

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Journal Article > Item 987654321/121471

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/121471

Title:	Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset
Authors:	Wei-Peng Tang;Sze-Teng Liong;Chih-Cheng Chen;Ming-Han Tsai;Ping-Cheng Hsieh;Yu-Ting Tsai;Shih-Hsin Chen;Kun-Ching Wang
Keywords:	automated surface inspection;convolutional neural network;multi-receptive field fusion network;lightweight dataset
Date:	2021-10-12
Issue Date:	2021-10-14 12:11:10 (UTC+8)
Publisher:	MDPI AG
Abstract:	With the advancement of industrial intelligence, defect recognition has become an indispensable part of facilitating surface quality in the steel manufacturing process. To assure product quality, most previous studies were typically trained with many defect samples. Nonetheless, a large quantity of defect samples is difficult to obtain, owing to the rare occurrence of defects. In general, deep learning-based methods underperformed as they have inherent limitations due to inadequate information, thereby restraining the application of models. In this study, a two-level Gaussian pyramid is applied to decompose raw data into different resolution levels simultaneously filtering the noises to acquire compact and representative features. Subsequently, a multi-receptive field fusion-based network (MRFFN) is developed to learn the hierarchical features and synthesize the respective prediction scores to form the final recognition result. As a result, the proposed method is capable of exhibiting an outstanding performance of 99.75% when trained using a lightweight dataset. In addition, the experiments conducted using the disturbance defect dataset showed the robustness of the proposed MRFFN against common noises and motion blur.
Relation:	Applied Sciences 11(20), p.9473
DOI:	10.3390/app11209473
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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