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Industrial Tool Recognition and Classification System under Intelligent Manufacturing

Author(s)

Zhanhong Liang, Shaoyong Hong*

Affiliation(s)

School of Artificial Intelligent, Guangzhou Huashang College, Guangzhou, China *Corresponding Author

Abstract

With the rapid development of industrial intelligence, traditional tool recognition technologies are increasingly unable to meet the requirements of high efficiency and precision in modern manufacturing. To overcome these limitations, this paper introduces a novel industrial tool recognition approach that combines convolutional neural networks (CNN) with computer vision to design the "Smart-Eye Scout" system. The proposed system processes real-time image data collected by industrial cameras, enabling robust recognition of tools in complex production environments while supporting visual interaction functions. The experimental evaluation demonstrates that the system achieves high recognition accuracy and real-time performance under conventional industrial conditions, although further optimization is necessary to address challenges such as extreme lighting and unfamiliar tool types. The main contribution of this study lies in the integration of deep learning and real-time computer vision into a practical recognition platform, providing an effective solution for industrial intelligence. This work offers significant potential for enhancing production efficiency and advancing intelligent manufacturing.

Keywords

Industrial Tool Recognition; Deep Learning; Convolutional Neural Networks (CNN); Computer Vision; Intelligent Manufacturing

References

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