Low-contrast Defects Research Articles

FDADNet: Detection of Surface Defects in Wood-Based Panels Based on Frequency Domain Transformation and Adaptive Dynamic Downsampling

The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning methods based on spatial domain processing often contain noise and blurred boundaries, which severely affects detection performance. To address these issues, we have proposed a wood-based panel surface defect detection method based on frequency domain transformation and adaptive dynamic downsampling (FDADNet). Specifically, we designed a Multi-axis Frequency Domain Weighted Information Representation Module (MFDW), which effectively decoupled the indistinguishable low-contrast defects from the background in the transform domain. Gaussian filtering was then employed to eliminate noise and blur between the defects and the background. Additionally, to tackle the issue of scale differences in defects that led to difficulties in accurate capture, we designed an Adaptive Dynamic Convolution (ADConv) module for downsampling. This method flexibly compressed and enhanced features, effectively improving the differentiation of the features of objects of varying scales in the transform space, and ultimately achieved effective defect detection. To compensate for the lack of data, we constructed a dataset of wood-based panel surface defects, WBP-DET. The experimental results showed that the proposed FDADNet effectively improved the detection performance of wood-based panel surface defects in complex scenarios, achieving a solid balance between efficiency and accuracy.

Thermal Battery Multi-Defects Detection and Discharge Performance Analysis Based on Computed Tomography Imaging

To address the typical structural defects that are prone to occur during the preparation and storage processes of thermal battery, experiments of battery image acquisition were designed based on X-ray computed tomography system. An improved Yolov5s network was employed to achieve high-precision automatic detection of typical defects. Through the discharge experiment of thermal battery, discharge performance curves of normal batteries and three defective batteries were constructed. The impact and mechanisms of different defects on the discharge performance were analyzed based on the voltage curve. By designing an automatic stitching scheme, the phenomenon of interlayer information overlap caused by the increase of cone angle in digital radiography images was suppressed. To address the issues of low image contrast and limited defect data in thermal battery imaging, the defect dataset was expanded using the designed image preprocessing steps and improving the contrast of the images. For subtle defects that are difficult to identify, the introduced multi-head self-attention mechanism in Transformer and the use of Focal Loss instead of cross-entropy loss function were employed to improve the recognition accuracy of subtle defects while ensuring the detection speed. The comparative experiment shows that the improved network model has higher recognition accuracy compared to Faster R-CNN, SSD, Cascade R-CNN, EfficientDet and the original Yolov5s network. The recognition accuracy of typical defects in thermal batteries can reach 98.7%.

A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

A reflectance-correction retinex framework for thermal image enhancement in nondestructive defect detection of CFRP

To ensure the quality of materials, pulsed thermography (PT) is an effective technique to detect the presence of internal defects in carbon fiber reinforced polymers (CFRP). However, non-uniform thermal excitation during the testing often leads to uneven heat distribution within the original thermal images, and the limited heat absorption by the surface of specimen may cause low-contrast defect signals that further impair the identification of defects. In response to these challenges, this study introduces an enhancement framework, termed Reflectance-Correction Retinex (RCR). In the RCR framework, the single-scale Retinex method is first applied to equalize the non-uniform background within a thermal image, and a sigmoid modification is proposed to adaptively amplify the contrast between defects and their background. The RCR framework is tested on PT images of man-made CFRP specimens. The experimental results show that our proposed method substantially enhances the visual quality of PT images, which facilitates precision and reliability in defect detection applications.

A real-time surface defects detection model via dual-branch feature extraction and dynamic multi-scale fusion attention

Surface defect detection is a challenging task in industrial manufacturing, and the detection method based on deep learning has become the mainstream trend in the industry. However, the task of detecting defects on transparent plastic bottles poses unique challenges due to their clear, smooth surfaces and minimal texture, which contribute to specular reflections and make small, low-contrast defects difficult to discern. To tackle these challenges, we introduce the BiD-YOLO framework, an advanced version of the YOLOv5 model, tailored for real-time surface defect detection on such substrates. This framework notably enhances the detection accuracy for small, low-contrast defects and minimizes miss rates. At its core, BiD-YOLO incorporates a dual-branch feature extractor (DBFE) that utilizes both standard, dilated convolutions and a specialized small-object feature extraction branch to capture a broad spectrum of contextual information, optimizing the detection of fine details. Additionally, the dynamic multi-scale pyramid fusion attention (DyMFA) block with coordinate attention (CA) effectively addresses the loss of information in small-scale defects—a typical shortfall in conventional attention mechanisms—thereby improving detection precision across both channel and spatial dimensions. To achieve an optimal balance between accuracy and computational efficiency, we introduce two versions of the dynamic multi-scale pyramid attention block: multi-scale pyramid channel attention and dynamic channel attention. We also curated and annotated the TPSD dataset, a specialized collection of images showcasing surface defects on transparent plastic bottles under diverse real-world production conditions. Together with the NEU-DET dataset, these resources were employed to validate the BiD-YOLO model's effectiveness. Experimental results demonstrate a substantial improvement in defect detection capabilities, achieving mean average precision (mAP) values of 63.1% and 85.6%, respectively. Moreover, the two working models exhibit FPS values of 47.23FPS and 50.21FPS respectively. These findings underscore the BiD-YOLO model's pivotal role in reducing oversight in defect detection, thereby enhancing quality control within industrial manufacturing environments. The BiD-YOLO framework represents a significant advancement in the automated detection of surface defects on transparent plastic bottles, merging innovative technical features with robust practical applicability for industrial use.

SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy.

A bilateral feature fusion network for defect detection on mobile cameras

Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing.

Improved STMask R-CNN-based defect detection model for automatic visual inspection of an optics lens.

A lens defect is a common quality issue that has seriously harmed the scattering characteristics and performance of optical elements, reducing the quality consistency of the finished products. Furthermore, the energy hotspots coming from the high-energy laser through diffraction of optical component defects are amplified step by step in multi-level laser conduction, causing serious damage to the optical system. Traditional manual detection mainly relies on experienced workers under a special light source environment with high labor intensity, low efficiency, and accuracy. The common machine vision techniques are incapable of detecting low contrast and complex morphological defects. To address these challenges, a deep learning-based method, named STMask R-CNN, is proposed to detect defects on the surface and inside of a lens in complex environments. A Swin Transformer, which focuses on improving the modeling and representation capability of the features in order to improve the detection performance, is incorporated into the Mask R-CNN in this case. A challenge dataset containing more than 3800 images (18000 defect sample targets) with five different types of optical lens defects was created to verify the proposed approach. According to our experiments, the presented STMask R-CNN reached a precision value of 98.2%, recall value of 97.7%, F1 score of 97.9%, mAP@0.5 value of 98.1%, and FPS value of 24 f/s, which outperformed the SSD, Faster R-CNN, and YOLOv5. The experimental results demonstrated that the proposed STMask R-CNN outperformed other popular methods for multiscale targets, low contrast target detection and nesting, stacking, and intersecting defects sample detection, exhibiting good generalizability and robustness, as well as detection speed to meet mechanical equipment production efficiency requirements. In general, this research offers a favorable deep learning-based method for real-time automatic detection of optical lens defects.

Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery

Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects’ size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection.

Rethinking unsupervised texture defect detection using PCA

In recent times, deep learning frameworks have become the preferred solution for visual object detection tasks, including texture defect detection. However, in real-world industrial applications, such as operation and maintenance of gas-insulated switchgear (GIS) equipment, collecting a large number of samples in advance is difficult because opening GIS equipment affects its normal operation; moreover, only four real-world GIS equipment defect samples are available currently. Most deep learning methods perform poorly when trained using a small number of labelled samples or when there is a lack of computational resources. In addition, the features extracted by multi-layer convolution and down-sampling are often unsuitable for small-size and low-contrast defects. However, the structure of the repetitive textured surface is simple and easy to model. Hence, this paper proposes a texture-defect detection method using principal components analysis (PCA) and histogram-based outlier score (HBOS) that requires only a small number of unlabelled samples and low computational complexity. First, PCA is utilized to extract features from the training patches to enhance the difference between the defect and background regions. Then, non-saliency suppression is applied for feature fusion to obtain the saliency map. Finally, the saliency value is converted to the defect score, and the defect is located by HBOS. Experiments on two public datasets and a GIS simulation equipment demonstrate that the proposed method is suitable for product surface defect detection and operation and maintenance in power distribution equipment. Moreover, compared with traditional texture feature-based and deep learning methods, the proposed method yields better results for small-size and low-contrast defects with only four unlabelled training samples per class.

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination.

Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.

Crosstalk Defect Detection Method Based on Salient Color Channel Frequency Domain Filtering.

Display crosstalk defect detection is an important link in the display quality inspection process. We propose a crosstalk defect detection method based on salient color channel frequency domain filtering. Firstly, the salient color channel in RGBY is selected by the maximum relative entropy criterion, and the color quaternion matrix of the displayed image is formed with the Lab color space. Secondly, the image color quaternion matrix is converted into the logarithmic spectrum in the frequency domain through the hyper-complex Fourier transform. Finally, Gaussian threshold band-pass filtering and hyper-complex inverse Fourier transform are used to separate the low-contrast defects and background of the display image. The experimental results show that the accuracy of the proposed algorithm reaches 96% for a variety of crosstalk defect detection. Compared with the current advanced defect detection algorithms, the effectiveness of the proposed method for low-contrast crosstalk defect detection is confirmed.

Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement

Light-emitting diode (LED) chips have disordered arrangement and defects with characteristics of low contrast, for which traditional segmentation methods cannot classify surface defects effectively. In this paper, a chip segmentation method based on position pre-estimation and a modified Normalized Correlation Coefficient (NCC) matching algorithm, as well as feature enhancement methods are proposed. The position pre-estimation method is used to avoid the interference introduced by the disordered chip arrangement and the large missing area. By modifying the NCC algorithm, matching speed is improved by eight times compared to traditional NCC while matching result is not affected by brightness change. Furthermore, feature enhancement schemes with higher speed and accuracy were designed to identify low-contrast defects. The experimental results showed that the average accuracy reached 99.54%, improved by 0.66% compared to the state-of-the-art method while the inspection missing rate was 0.03%. In addition, the detection time of a single chip was approximately 1.098 ms, which meets the requirements of online detection, and the smallest defect that could be detected was 2 µm. In summary, the methods proposed in this study meet the requirements of industrial online detection regardless of accuracy, efficiency, or extensibility.

EMRA-Net: A Pixel-Wise Network Fusing Local and Global Features for Tiny and Low-Contrast Surface Defect Detection

The problem of tiny and low-contrast surface defect detection is a nontrivial one. To solve the problems, this article proposes an edge and multi-scale reverse attention network (EMRA-Net), of which includes feature extraction and feature fusion. In the process feature extraction, the global dynamic convolution features, global dynamic multi-scale fusion (MSF) feature, and local pyramid edge feature are obtained through the pre-training backbone Resnet 34, the fresh MSF module, and the innovative pyramid edge module, respectively. In the process of feature fusion, these features are blended by a new self-learning scale module and a novel spatial channel domain reverse attention (SCRA) module step by step. The experimental results of five widely used datasets show that the EMRA-Net outperforms the existing methods. In addition, the mean intersection of union (mIoU)<xref ref-type="fn" rid="fn1">¹</xref><xref ref-type="fn" rid="fn2">²</xref> on the printed circuit board (PCB) industry dataset reaches 95.31&#x0025;. Moreover, the results of EMRA-Net indicated that the local edge feature can improve the performance of the defect detection network. The EMRA-Net has great potential in the application in the detection of tiny and low-contrast defects.

Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects

Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods have made huge leaps, they still confront a lot of challenges such as defect scale variation, complex background, low contrast, and so on. To address these issues, we propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder-decoder network. JAFFNet mainly incorporates a joint attention-guided feature fusion (JAFF) module into decoding stages to adaptively fuse low-level and high-level features. The JAFF module learns to emphasize defect features and suppress background noise during feature fusion, which is beneficial for detecting low-contrast defects. In addition, JAFFNet introduces a dense receptive field (DRF) module following the encoder to capture features with rich context information, which helps detect defects of different scales. The JAFF module mainly utilizes a learned joint channel-spatial attention map provided by high-level semantic features to guide feature fusion. The attention map makes the model pay more attention to defect features. The DRF module utilizes a sequence of multi-receptive-field (MRF) units with each taking as inputs all the preceding MRF feature maps and the original input. The obtained DRF features capture rich context information with a large range of receptive fields. Extensive experiments conducted on SD-saliency-900, Magnetic tile, and DAGM 2007 indicate that our method achieves promising performance in comparison with other state-of-the-art methods. Meanwhile, our method reaches a real-time defect detection speed of 66 FPS.

Low-Contrast Defects Recognition Using Low-Order Residual Network

Low-contrast defects recognition is a dramatically difficult issue in the field of image recognition. The traditional machine vision method is mainly suitable for defects with obvious feature differences. In recent years, machine learning techniques have been successfully applied to the image analyses, and the deep learning methods provide new solutions for challenging problems in many areas. In this paper, a deep learning network framework based on the low-order residual network is proposed to detect low-contrast defects. Especially, a low-order feature extraction module is designed in order to effectively extract target features with low contrast and small size. The low-contrast watermark defects on complementary metal-oxide-semiconductor transistor (CMOS) camera modules are collected as the test objects to validate the effectiveness of the proposed method. The gray differences between the watermark defects and their adjacent areas are generally several gray-levels. The experimental results show that compared with the existing advanced classification neural network algorithms, the proposed method can effectively identify the watermark defects with a recognition accuracy of over 89%.

An Accurate Mura Defect Vision Inspection Method Using Outlier-Prejudging-Based Image Background Construction and Region-Gradient-Based Level Set

The visual inspection of Mura defects is still a challenging task in the quality control of panel displays because of the intrinsically nonuniform brightness and blurry contours of these defects. The current methods cannot detect all Mura defect types simultaneously, especially small defects. In this paper, we introduce an accurate Mura defect visual inspection (AMVI) method for the fast simultaneous inspection of various Mura defect types. The method consists of two parts: an outlier-prejudging-based image background construction (OPBC) algorithm is proposed to quickly reduce the influence of image backgrounds with uneven brightness and to coarsely estimate the candidate regions of Mura defects. Then, a novel region-gradient-based level set (RGLS) algorithm is applied only to these candidate regions to quickly and accurately segment the contours of the Mura defects. To demonstrate the performance of AMVI, several experiments are conducted to compare AMVI with other popular visual inspection methods are conducted. The experimental results show that AMVI tends to achieve better inspection performance and can quickly and accurately inspect a greater number of Mura defect types, especially for small and large Mura defects with uneven backlight. Note to Practitioners —The traditional Mura visual inspection method can address only medium-sized Mura defects, such as region Mura, cluster Mura, and vertical-band Mura, and is not suitable for small Mura defects, for example, spot Mura. The proposed accurate Mura defect visual inspection (AMVI) method can accurately and simultaneously inspect not only medium-sized Mura defects but also small and large Mura defects. The proposed outlier-prejudging-based image background construction (OPBC) algorithm of the AMVI method is employed to improve the Mura true detection rate, while the proposed region-gradient-based level set (RGLS) algorithm is used to reduce the Mura false detection rate. Moreover, this method can be applied to online vision inspection: OPBC can be implemented in parallel processing units, while RGLS is applied only to the candidate regions of the inspected image. In addition, AMVI can be extended to other low-contrast defect vision inspection tasks, such as the inspection of glass, steel strips, and ceramic tiles.

An efficient detection algorithm based on anisotropic diffusion for low-contrast defect

In this paper, we propose an efficient algorithm based on the anisotropic diffusion model to detect defect in a low-contrast surface image, especially aimed at anti-reflective (AR) glass. The proposed algorithm has two important procedures: (1) a modified anisotropic diffusion model and (2) morphological directivity filter. The modified diffusion model based on an adaptive edge threshold, the standard score (Z-score), is proposed to quickly and efficiently enhance the low-contrast defects. It acts as the adaptive enhancement process. The pixels with both the low gradient and the high Z-score or the high gradient and the low Z-score will generate a high diffusion coefficient. It enhances the gray levels of suspected defective edges and also preserves the original gray levels of an internal area of suspected defects, as well as generates the slight smoothing process for the noisy background. A simple and efficient method can easily segment the defects, followed by the effective morphological directivity filter, which removes noise from the thresholding image. The proposed algorithm is evaluated by a set of 23 low-contrast surface images of AR glass in this study. The experimental results show that the proposed algorithm is superior to the four competitive approaches. The defect detection results demonstrate that the proposed algorithm can segment the complete defects. In addition, the computational advantage compared to the Perona and Malik model, C-T model 1, C-T model 2, and histogram statistics model are about 4.48, 2.26, 19.53, and 26.63 times, respectively. It can be concluded that the proposed algorithm not only provides reliable inspection results but also improves the inspection efficiency over 2 times.

Defect detection in magnetic tile images based on stationary wavelet transform

A novel approach using stationary wavelet transform (SWT) is proposed for automatically detecting low-contrast defects under various light conditions in magnetic tile images. In this method, the uneven background was removed by Sobel operation. Then the index low-pass filtering and the nonlinear enhancement were respectively used to eliminate the interference and enhance the target in subbands produced by SWT. To verify the validity of the proposed algorithm, extensive experiments were conducted in a novel machine vision based system. As the result shows, the proposed method achieves an accuracy rate of 92.86% in detecting various defects in magnetic tile surfaces with the average operation time of 0.5190s, and is superior to traditional methods in terms of the high reliability and accuracy.

Detection of Low-Contrast Defects in Optical Films Using Sigma-Based Segmentation Technique

Optical film is an important component for LCDs, cell phones and other mobile devices. It is crucial to ensure an optical films is defect-free. However, derive from the inherent translucent characteristic, many images of defects of optical films are low contrast. Low-contrast defects are notorious for their difficult to be detected. As some low-contrast defects are barely visible to human being without special training, it is indeed a challenging task to capture low-contrast defect images. To overcome the difficulty, we thoroughly investigated various of imaging and lighting technologies. After images have been captured successfully, we devided the captured defects, based on their sizes, into two categories, i.e. macro defects and micro defects. To detect both large and small defects, we set up a macro inspection system and a micro inspection system, respectively. In the study, we provide an easy to understand approach to reveal low-contrast defects. The experimental results showed that the proposed inspection system and method is capable of detecting low-contrast defects such as uneven coatings, streaks, white spots and foreign particles.