Kong Defeng, Hu Xinyu, Zhang Junwei, Liu Xiyang, Zhang Daode
School of Mechanical Engineering, Hubei University of Technology, 28 Nanli Road, Hongshan District, Wuhan City, Hubei Province 430068, China.
iScience. 2024 Feb 5;27(3):109147. doi: 10.1016/j.isci.2024.109147. eCollection 2024 Mar 15.
Aiming at the current SPI (solder paste inspection) system for printing solder paste similar defects detection accuracy is not high, the system intelligence degree is low and so on, design a for the solder paste similar defects and combined with phase modulation profile measurement technique and improve the YOLOX intelligent detection system. The core of the system is the improved YOLOX depth model based on s-mosica and kt-iou algorithms proposed in this paper. The experimental results show that the proposed s-mosica and kt-iou algorithms can effectively improve the detection accuracy of printed solder paste, and when combined with the YOLOX model, the best 90.33% detection accuracy is obtained, which is better than the detection performance of the existing algorithms in the same scenario, and it provides an effective and feasible reference program for the design of the SPI high-precision intelligent detection system.
针对当前SPI（焊膏检测）系统对印刷焊膏类似缺陷检测精度不高、系统智能化程度低等问题，设计了一种针对焊膏类似缺陷并结合相位调制轮廓测量技术的改进YOLOX智能检测系统。该系统的核心是本文提出的基于s-mosica和kt-iou算法的改进YOLOX深度模型。实验结果表明，所提出的s-mosica和kt-iou算法能够有效提高印刷焊膏的检测精度，与YOLOX模型结合时，获得了最佳90.33%的检测精度，优于同场景下现有算法的检测性能，为SPI高精度智能检测系统的设计提供了有效可行的参考方案。
