Tag Identification for Vehicles by Using Connected Components based Segmentation and Template Matching Based Recognition

Srisaila, A.; Kranthi, S.; Pranathi, K.; Latha, P.

doi:10.1109/iceca.2018.8474774

Cited by 2 publications

(1 citation statement)

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“…Huang et al proposed a standard machine assembly quality machine vision method based on One Versus Rest One Versus Rest (OVR-SVM) and realized the assembly quality evaluation of standard components based on the support vector machine by using the One Versus Rest (OVR) strategy [7]. Srisaila et al proposed utilizing the connection component and template matching technology to solve the problems of image blur and uneven brightness, and complete image segmentation and matching tasks [8]. The template matching algorithm is simple, and highly effective detection can be achieved under ideal conditions, but it is difficult to obtain better detection effects using it in cases of rotation or size change of the matching target in the original image.…”

Section: Introductionmentioning

confidence: 99%

Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation

Liu

et al. 2019

Symmetry

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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.

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Section: Introductionmentioning

confidence: 99%