Abstract: In recent years, furniture panel processing positioning based on computer vision technology has received increasing attention. A 3D measurement imaging technology based on laser scanning technology is proposed to address the significant environmental impact of traditional visual technology. Subsequently, deep image processing techniques are introduced to address the high image noise. In the experiment of measuring panel using 3D measurement technology, 14 measurement lines were taken every 10mm of the measurement length. The maximum measurement value was 204.62mm, the minimum measurement value was 204.37mm, and the manual measurement result was 204.5mm. 14 measurement lines were taken every 14mm of the measurement length. The maximum measurement value was 134.15mm, the minimum measurement value was 133.894mm, and the manual measurement was 134.1mm. 14 measurement lines were taken every 14mm of the measurement thickness. The maximum measurement value was 26.646mm, the minimum measurement value was 26.242mm, and the result of manual measurement was 26.5mm. The 3D imaging technology based on laser scanning is relatively accurate in measuring the 3D data of panels, which can be applied in the positioning and detection system of panel processing. In addition, the experiment compares depth image processing methods, verifying the effectiveness of the designed method. Meanwhile, this research also has certain reference significance for exploring the real-time positioning of other objects.

Binglu Chen, Guanyu Chen and Qianqian Hu, “Furniture Panel Processing Positioning Design Based on 3D Measurement and Depth Image Technology” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150979

@article{Chen2024,
title = {Furniture Panel Processing Positioning Design Based on 3D Measurement and Depth Image Technology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150979},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150979},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Binglu Chen and Guanyu Chen and Qianqian Hu}
}