The Application of Image Recognition Technology Based on Deep Learning in Data Analysis Article Swipe

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Wei Shi , Kai Guo , Weilan Liu , Jingwei Guo ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.12694/scpe.v26i3.4235

To address the challenge of achieving high recognition accuracy across various image types, the author advocates for applying deep learning-based image recognition technology in data analysis research. The author first uses convolutional neural networks to train and process the raw laser image big data, extract image features, and set a threshold for pre segmentation to complete image preprocessing; then use the regularized least squares method to complete the laser image pattern recognition process and achieve image pattern differentiation; finally, construct an experimental section and analyze the application effect of this method. The experimental results show that the recognition rate of the preset target images for different types of images is stable at over 96%, the recognition error rate is stable at less than 2%, and the image recognition time is within 15 seconds, indicating that the method has good application effects. This method has a shorter recognition time and higher efficiency, providing impetus for the improvement of laser image analysis and processing technology.

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Computer Science

Artificial Intelligence

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Computer science Artificial intelligence Deep learning Image (mathematics) Pattern recognition (psychology) Computer vision Machine learning Data mining Data science

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Type: article
Language: en
Landing Page: https://doi.org/10.12694/scpe.v26i3.4235
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DOI: https://doi.org/10.12694/scpe.v26i3.4235

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Title: The Application of Image Recognition Technology Based on Deep Learning in Data Analysis

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Language: en

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Publication year: 2025

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Publication date: 2025-04-01

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Authors: Wei Shi, Kai Guo, Weilan Liu, Jingwei Guo

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Landing page: https://doi.org/10.12694/scpe.v26i3.4235

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Concepts: Computer science, Artificial intelligence, Deep learning, Image (mathematics), Pattern recognition (psychology), Computer vision, Machine learning, Data mining, Data science

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