The assembly quality of panel screws has an important impact on the performance and appearance quality of products.In response to the issue of assembly quality detection for panel screws,an improved Fast Region based Convolutional Neural Network(Faster R-CNN)algorithm and a heuristic hash matching algorithm based on screw visual detection were introduced.The results show that the average accuracy of the proposed algorithms is stable at over 99.3％,and the fluctuation range does not exceed 0.3％.Compared with the Shape Based matching operator,the average detection ACC of the three piano panel screws increased by 6.04％,2.65％,and 4.25％,respectively.The improved Faster R-CNN model with dual feature fusion has significant performance advanta-ges in target detection of piano panel screws,and the heuristic hash matching algorithm based on screw visual detection can accurately identify the assembly situation of piano panel screws,greatly improving detection efficiency and accuracy.It has important practical significance for improving assembly quality,production efficiency,product safety,and reducing production costs.
关键词
区域相关哈希编码/面板螺钉/装配质量/机器视觉检测
Key words
region related hash encoding/panel screws/assembly quality/machine vision inspection
维普
万方数据