Practice and Optimization of Image Recognition Technology for Higher Vocational Computer Application Majors Based on Deep Learning: Taking Industrial Product Defect Detection as an Example

This article focuses on the field of computer application majors in higher vocational and technical colleges. It deeply expounds on the case applications of deep learning and related technologies in image recognition technology, centering around the actual enterprise requirements and technological requirements of industrial product defect detection. By building different deep learning models and related technologies, this article collects product image data in enterprise production, thus completing training, testing, and evaluation. Secondly, it compares the differences of each model in key indicators such as detection accuracy and recall rate, and combines with chart analysis to visually display the changes of the loss function and the accuracy improvement curve during the model training process. The design and analysis of the case can effectively enhance the practical operation and technical application abilities of higher vocational students in image recognition technology, and also provide case support for exchanges among industry practitioners.

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Publication Details

Journal
Academic Journal of Science and Technology
Published
2025-05-14
DOI
https://doi.org/10.54097/n6e7n341
Primary Topic
Medical Research and Treatments
Type
article
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article

Practice and Optimization of Image Recognition Technology for Higher Vocational Computer Application Majors Based on Deep Learning: Taking Industrial Product Defect Detection as an Example

Lian Dan
Academic Journal of Science and Technology
Medical Research and Treatments
article

Practice and Optimization of Image Recognition Technology for Higher Vocational Computer Application Majors Based on Deep Learning: Taking Industrial Product Defect Detection as an Example

Lian Dan
article en

Abstract

This article focuses on the field of computer application majors in higher vocational and technical colleges. It deeply expounds on the case applications of deep learning and related technologies in image recognition technology, centering around the actual enterprise requirements and technological requirements of industrial product defect detection. By building different deep learning models and related technologies, this article collects product image data in enterprise production, thus completing training, testing, and evaluation. Secondly, it compares the differences of each model in key indicators such as detection accuracy and recall rate, and combines with chart analysis to visually display the changes of the loss function and the accuracy improvement curve during the model training process. The design and analysis of the case can effectively enhance the practical operation and technical application abilities of higher vocational students in image recognition technology, and also provide case support for exchanges among industry practitioners.

Academic Journal of Science and TechnologyVol. 15(2)
Zhejiang DongFang Vocational and Technical College (CN)
Openalex Percentile: Top 84%
Medical Research and Treatments
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