Wavelet Subband-Based Tensor for Smartphone Physical Button Inspection

Abstract

A smartphone contains many critical components that are produced in highly automated and precisely monitored facilities throughout the complex manufacturing process. Even with the rapid development in the smartphone manufacturing industry today, the physical buttons are still existing on the smartphone because of the crucial importance of both in terms of their functionality and role. The smartphone’s physical buttons are small in size and have non-planar and shiny surfaces that lead to difficulty in detecting defects not only with human eyes but also with most AOI systems. Besides, most defects are tiny, with low contrast which is a huge challenge for deep learning models-based defect detection. To overcome these challenges, we propose a novel framework based on machine vision named highlight defect region by using higher-order singular value decomposition of wavelet subband-based tensor (HHoWST) for real-time smartphone’s physical buttons quality inspection. First, a modern image acquisition system is designed to obtain a high-quality smartphone’s physical button image dataset with a total of 500 images containing 13,472 samples of six defect types. Next, a wavelet subband-based third-order tensor of the smartphone’s physical button color image is constructed. Finally, higher-order singular value decomposition is proposed to estimate the components that contain the global illumination information and highlight the defective regions of the image. The experiments performed on HHoWST images reveal that our proposed method significantly improves the defect detection efficiency of deep learning models, such as SSD, Faster R-CNN, and YOLOv5, especially the performance in detecting the tiny defect types.

Keywords

• Automatic optical inspection
• defect detection
• higher-order singular value decomposition
• tensor
• wavelet transform
• deep learning model