Abstract
Chassis assembly quality is a necessary step to improve product quality and yield. In recent years, with the continuous expansion of deep learning method, its application in product quality detection is increasingly extensive. The current limitations and shortcomings of existing quality detection methods and the feasibility of improving the deep learning method in quality detection are presented and discussed in this paper. According to the characteristics of numerous parts and complex types of chassis assembly components, a method for chassis assembly detection and identification based on deep learning component segmentation is proposed. In the proposed method, assembly quality detection is first performed using the Mask regional convolutional neural network component instance segmentation method, which reduces the influence of complex illumination conditions and background detection. Next, a standard dictionary of chassis assembly is built, which is connected with Mask R-CNN in a cascading way. The component mask is obtained through the detection result, and the component category and assembly quality information is extracted to realize chassis assembly detection and identification. To evaluate the proposed method, an industrial assembly chassis was used to create datasets, and the method is effective in limited data sets of industrial assembly chassis. The experimental results indicate that the accuracy of the proposed method can reach 93.7%. Overall, the deep learning method realizes complete automation of chassis assembly detection.
Highlights
Background of the Proposed MethodIn this study, Mask regional CNN (R-CNN) was first introduced into the basic framework of Faster R-CNN to achieve pixel-level segmentation
In order to find a simpler and more effective detection and identification method, this paper proposes the adoption of a non-reference method
For each proposal box of Faster R-CNN, full convolutional network (FCN) is used for semantic segmentation, and the segmentation task is performed simultaneously with positioning and classification tasks [29,31,32]
Summary
Mask R-CNN was first introduced into the basic framework of Faster R-CNN to achieve pixel-level segmentation. The SD corresponding to the chassis model was built, and the SD Mask R-CNN model established. This method incorporates the full convolutional network (FCN) and region of interest align (ROIAlign) techniques to achieve accurate segmentation of the chassis image and processing of corresponding pixel values [29]
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