Assembly state detection based on deep learning and object matching

Abstract

To improve assembly quality and efficiency, a method based on deep learning and object matching is proposed to detect missing and wrong parts. An improved YOLO V3 neural network is designed to solve the problem of missing assembly. A small target detection scale and attention module is added to the neural network. The size of prior anchor box is optimized by K-means++ clustering algorithm. For the problem of wrong assembly, the standard assembly state detection template is constructed according to the virtual assembly scene in CAD software, and the 2D detection box of the current assembly object in the scene image is matched with the 2D box in the standard state template based on IoU (Intersection over Union) calculation. The assembly model MONA (a 3D model for the evaluation of manual Assembly tasks), is used to test the proposed method. Experimental results show that this method can accurately locate and identify assembly parts, and effectively detect the missing and wrong parts in the assembly process.