Design of intelligent inspection system for solder paste printing defects based on improved YOLOX

Abstract

Summary: 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.

Keywords

• Machine learning
• Engineering