Web-based automatic picture object recognition system

Jingxin Wang; Xin Pan

doi:10.1117/12.3012209

21 December 2023 Web-based automatic picture object recognition system

Jingxin Wang, Xin Pan

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297024 (2023) https://doi.org/10.1117/12.3012209
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In the process of AI object recognition, objects need to be labeled in images, there are even multiple objects to be labeled in a single image, and the images needed for training reach thousands or even tens of thousands, which makes setting up sample integration a workload. Therefore, this paper proposes a Web-based automatic image object recognition system, which collects data based on the Web and allows multiple people to collaborate in labeling targets in images, and after finishing labeling, aggregates labeled images and builds a sample set; on this basis, the YOLO neural net model has trained automatically, and the study shows that this system can greatly improve the efficiency of sample set construction and quickly build a deep target. The study shows that this system can greatly improve the efficiency of sample set construction and quickly build a deep target recognition neural net, which has good application value.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingxin Wang and Xin Pan "Web-based automatic picture object recognition system", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297024 (21 December 2023); https://doi.org/10.1117/12.3012209

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KEYWORDS

Education and training

Data modeling

Target recognition

Target detection

Deep learning

Statistical modeling

Neural networks

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