Defect Detection System for Smartphone Front Camera Based on Improved Template Matching Algorithm

Automatic defect detection plays crucial role in resilient manufacturing in terms of product quality and cost effectiveness. With reference to the smartphone front cameras production process, the most recurrent defects can be classified into no hole, inner hole burr, outer circle damage, hole deformation, outer circle fracture and hole position offset. Due to the fast production lines and the defects micro size, Sampling-based methods has huge uncertainty and limitation, and Machine learning-based methods are characterised by low efficiency. To tackle these issues, this paper proposes a machine vision-based detection methods of smartphone front camera based on a multi-step template matching algorithm to reduce the computational effort. Specifically, in order to improve the algorithm efficiency, the images of the smartphone front cameras, acquired using industrial image acquisition devices are pre-processed by performing Hough circle and line transformations respectively, then locate the exact defect area as a region of interest (ROI). Finally, a multi-step template matching algorithm is used to detect and classify a number of common defects. Experimental results show an excellent suitability of the proposed system in detecting front camera surface defects. A benchmarking with other available technologies highlights how the proposed system yields an improvement in the detection speed by 46%, along with an improvement in the detection accuracy by 9%. The successful industrial implementation is discussed with reference to the integration into an automatic defect detection system in a smartphone front camera manufacturing context