Noise free defect detection in ceramic tableware using a portable digital holographic camera

Deep learning assisted non-contact defect identification method using diffraction phase microscopy

Reliable detection of defects from optical fringe patterns is a crucial problem in non-destructive optical interferometric metrology. In this work, we propose a deep-learning-based method for fringe pattern defect identification. By attributing the defect information to the fringe pattern's phase gradient, we compute the spatial phase derivatives using the deep learning model and apply the gradient map to localize the defect. The robustness of the proposed method is illustrated on multiple numerically synthesized fringe pattern defects at various noise levels. Further, the practical utility of the proposed method is substantiated for experimental defect identification in diffraction phase microscopy.

Automotive tire defect characterization with a portable digital holographic camera with an extended field of view

Detection of tire defects is of prime importance for the on-road safety of vehicles. Hence, a quick non-invasive mechanism is required for frequent testing of tires in service as well as for quality checks of newly manufactured tires in the automotive industry. We propose a methodology based on the principle of double-exposure digital holographic interferometry, implemented with a portable digital holographic camera for successful detection and dimension measurement of tire defects. To implement the principle, mechanical load is applied to a tire to produce interferometric fringes by comparing the normal and stressed states of the tire surface. The defects in the tire sample are identified from discontinuities in the interferometric fringes. Quantitative analysis of the displacement of the fringes gives the dimensions of the defects. Some experimental results are presented, validated by a vernier caliper.

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China's decorative ceramics industry.