Printed circuit boards (PCBs) are a nearly ubiquitous component of every kind of electronic device. With the rapid development of integrated circuit and semiconductor technology, the size of a PCB can shrink down to a very tiny dimension. Therefore, high-precision and rapid defect detection in PCBs needs to be achieved. This paper reviews various defect detection methods in PCBs by analysing more than 100 related articles from 1990 to 2022. The methodology of how to prepare this overview of the PCB defect detection methods is firstly introduced. Secondly, manual defect detection methods are reviewed briefly. Then, traditional image processing-based, machine learning-based and deep learning-based defect detection methods are discussed in detail. Their algorithms, procedures, performances, advantages and limitations are explained and compared. The additional reviews of this paper are believed to provide more insightful viewpoints, which would help researchers understand current research trends and perform future work related to defect detection.

Non-uniform illuminated images have poses challenges in contrast enhancement due to the existence of different exposure region caused by the uneven illumination. Although Histogram Equalization (HE) is a well-known method for contrast improvement, however, existing HE-based enhancement methods for non-illumination often generated unnatural images, introduced unwanted artefacts and washed out effect because they do not utilize the information from the different exposure regions in performing equalization. Therefore, this study proposes a modified HE-based contrast enhancement technique for non-uniform illuminated images namely Exposure Region-Based Multi-Histogram Equalization (ERMHE). ERMHE uses exposure region-based histogram segmentation thresholds to segment the original histogram into sub-histograms. With the thresholded sub-histograms, ERMHE then uses an entropy-controlled gray level allocation scheme to allocate new output gray level range and to obtain new thresholds that will be used to repartition the histogram prior to HE process. 154 non-uniform illuminated sample images are used to evaluate the application of the proposed ERMHE. By comparing ERMHE to four existing HE-based contrast enhancement namely, Global HE, Mean Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE), qualitatively, ERMHE produces enhanced images with a natural appearance, appealing contrast, less degradation and reasonable detail preservation. Quantitatively, ERMHE achieves the highest Peak Signal to Noise Ratio (PSNR), lowest Absolute Mean Brightness Error (AMBE) and second best in Discrete Entropy (DE) scores. From the analyses, ERMHE has shown its capability in enhancing different exposure regions exist in a non-uniform illuminated images.

Composite such as Glass Fibre Reinforced Polymer (GFRP) is increasingly used as insulation in many industrial applications such as the steel pipelines in the oil and gas industry. Due to ageing and cyclic operation, many hidden defects exist under insulation, such as corrosion and delamination. If these defects are not promptly detected and restored, the growth of defects causes a catastrophic loss. Therefore, an effective inspection technique using non-destructive testing (NDT) to detect the underneath defect is required. The ability of microwave signals to penetrate and interact with the inner structure within composites makes them a promising candidate for composite inspection. In the case of GFRP, the random patterns cause permittivity variations that influence the propagation of the microwave signals, which results in a blurred spatial image making the assessment of the material's state difficult. In this research, a novel microwave NDT technique is presented based on k-means unsupervised machine learning for defect detection in composites. At present, the defect evaluation using an unsupervised machine learning-based microwave NDT technique is not reported elsewhere. The unsupervised machine learning is employed to enhance the imaging efficiency and defect detection in GFRP. The technique is based on scanning the composite material with an open-ended rectangular waveguide operating from 18 to 26.5 GHz with 101 frequency points. The influence of the permittivity variations on the reflected coefficients due to the random patterns of GFRP is mitigated by measuring the mean of a set of the adjacent points at each operating frequency point using a small rectangular window. The measured data is converted to the time domain using a fast inverse Fourier transform (IFFT) to provide significant features and increase the signal resolution to 201-time steps. K-means algorithm is utilized to cluster the given features into the defect and defect-free regions in GFRP. The findings presented in this paper demonstrate the benefits of an unsupervised machine learning to detect a defect down to 1 mm, which is a considerable contribution over any existing defect inspection technique in composites.