Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework

• Published in: Journal of intelligent manufacturing 2022-06, Vol.33 (5), p.1373-1392
• Main Authors: Li, Feiyang, Cai, Nian, Deng, Xueliang, Li, Jiahao, Lin, Jianfa, Wang, Han
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Business and Management; Ceramics industry; Control; Control systems; Engraving; Inspection; Localization; Machines; Manufacturing; Mechatronics; Membranes; Neural networks; Photometry; Processes; Production; Production lines; Robotics; Theoretical analysis; Wastewater disposal; Wastewater treatment; Water treatment
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-020-01730-7

The ceramic membrane plays an important role in the wastewater disposal industry. The serial number engraved on each ceramic membrane is an essential feature for identification. Here, an automatic inspection system for serial numbers of ceramic membranes is proposed to replace the manual inspection. To the best of our knowledge, this is the first attempt to automatically inspect serial numbers of ceramic membranes. To suppress error accumulation inherently existed in the previous stepwise approaches, an end-to-end photometric-induced convolutional neural network framework is proposed for this automatic inspection system. The framework consists of three sequential stages, which are photometric stage for performing photometric stereo, localization stage for localizing the text region, and recognition stage for producing recognition results. The photometric stage can integrate three-dimensional shape information of serial numbers of ceramic membranes into the framework to improve the inspection performance. Since three stages are jointly trained, a theoretical analysis on the contributions of the local losses is provided to ensure the convergence of the framework, which can guide the design of the total loss function of the framework. Experimental results demonstrate that the proposed framework achieves better inspection performance with a reasonable inspection time compared with the state-of-the-art deep learning methods, whose localization performance and recognition performance are the F-score of 95.61% and the accuracy of 96.49%, respectively. Furthermore, these demonstrate the potential that our proposed automatic inspection system will be beneficial for the intelligentialize of the ceramic membrane manufacturing and wastewater treatment if it is equipped with a perception system and a control system in ceramic membrane production lines and wastewater treatment processes.