Vision-based adaptive stereo measurement of pins on multi-type electrical connectors

As a non-contact inspection approach, vision technology usually undertakes the task of positioning, measuring and defect identification in the field of industrial automation. However, traditional visual programs at a high price are often designed for only a single category of products. Furthermore, the quantitative measurement tasks in the industry usually require a rigorous visual environment as well as hardware equipment, which implies a lack of generalization. Hence, it is imperative to establish a robust approach to break the barriers of multi-type product inspection, while reducing both system complexity and costs. This paper proposed an adaptive approach that performs inspections of the pins' position for multi-type connectors. A joint strategy of deep neural network and pattern matching based on prior knowledge registration is constructed to achieve rapid positioning of sub-elements arranged in the target. Then, a hierarchical extraction method is designed to analyze features with various appearances and improve the anti-interference of vision-based system. The 3D version of the registration algorithm is embedded into the framework to determine abnormal positions of spatial data without reference. The proposed algorithm demonstrates a successful inspection of a total of 33 types of connectors, significant measurement robustness and adaptivity to the target pose, imaging status and feature diversity.