Empirical Research on Emotion Recognition in Reducing Product Defect Rates

Product quality control in industrial manufacturing is one of the key factors to ensure the competitiveness of enterprises. With the development of artificial intelligence technology, emotion recognition technology, as an innovative application of industrial vision, is gradually demonstrating its potential in the field of quality control. This article reviews the research on the application of emotion recognition technology to reduce product defect rates and discusses its practical utility and challenges within industrial vision systems. Firstly, the article outlines the basic principles of emotion recognition technology and its application scenarios in an industrial environment. It then provides a detailed analysis of how emotion recognition technology can monitor the emotional state of operators and early-warn potential operational errors caused by emotional fluctuations, thereby reducing product defects. Furthermore, the review also addresses the main challenges currently faced by emotion recognition technology in industrial applications, including accuracy, real-time performance, and adaptability to different cultures and individual differences. Through a comprehensive analysis of existing literature, this paper highlights the potential of emotion recognition technology in improving production efficiency and product quality, while also emphasizing the key technological issues and ethical considerations that future research needs to address.