Surface Defect Detection Method Based on a Small Number of Labeled Images

We present a novel method for small surface defect detection based on the spatially filtered dynamic speckles. This method possesses high fidelity and versatility. Factors defining resolution of the proposed method is estimated and it is shown that resolution depends solely on the geometrical parameters of the optical system. Experimental results demonstrate feasibility of the proposed method for surface defect detection and are in good agreement with the theoretical estimations. A prototype for online detection of the defects with diameter of 400 µm is presented. Ways to improve prototype performance and the method resolution are discussed.

QRNet: Query-based reparameterization net for real-time detection of power adapter surface defects

Surface defect detection is challenging due to varying defect types and their novelties. Because of this, it is hard for algorithms to implement across datasets. Moreover, current automated optical inspection (AOI) machines cannot handle this novelty effectively. In this work, we develop a new method for surface defect detection based on generative models, which can detect novelty according to learned distributions. Experimental results on real industrial datasets show that the proposed method can successfully construct the surface texture pattern generator. By transforming the image through the generator to the corresponding latent space, the defects can be separated effectively without a tedious effort of annotation in a large amount of training data.

At present, the detection of surface defects of paper-plastic composite bags still mainly depends on manual visual inspection, which is very inefficient and easy to cause false detection. Based on the algorithm which combines edge detection and adaptive region growth, binary images of the surface defect areas is extracted, six types of shape features and seven types of invariant moment features are defined by calculation as the basis to complete the effective extraction of the surface features, then a paper-plastic composite bags surface defect detection platform is built to complete the defect detection experiment, the defect screening experiment was completed by the rapid screening method of its surface defect image based on gradient projection difference. At the same time, based on Genetic Algorithm (GA), Particle Swarm Optimization, and Grey Wolf Optimization, parameter optimization models of Support Vector Machine were established, respectively. Through experimental comparison, some results show that the defect detection and classification accuracy rate reach 96.83% based on the GA, and the detection speed with the screening method is 3.75 times faster than that without the screening method. Therefore, the reliability of the image classification and screening method for bags surface defects proposed in this study is verified.

In order to solve the problems of low recognition rate, incapability of autonomous detection and weak generality of the existing surface defect detection methods, an improved depth learning surface defect detection method is proposed. This method improves the convolution neural network model in depth learning and divides it into two modules: segmentation module and decision module. After preprocessing, the image is input to the segmentation module for training, and then the output of the segmentation module and network features are used as input to the decision module to detect defects in the image. In the improved model, the convolution layer and convolution kernel size in the segmentation module are optimized, and a new convolution network model is constructed. In downsampling, the maximum pool is used instead of the maximum stride, and the loss function and activation function are designed at the same time. Experiments show that the method has a high defect detection accuracy rate of 99%, realizes autonomous detection, and has certain universality.

Traditional manual inspection approaches face challenges due to the reliance on the experience and alertness of operators, which limits their ability to meet the growing demands for efficiency and precision in modern manufacturing processes. Deep learning techniques, particularly in object detection, have shown significant promise for various applications. This paper proposes an improved YOLOv11-based method for surface defect detection in electronic products, aiming to address the limitations of existing YOLO models in handling complex backgrounds and small target defects. By introducing the MD-C2F module, DualConv module, and Inner_MPDIoU loss function, the improved YOLOv11 model has achieved significant improvements in precision, recall rate, detection speed, and other aspects. The improved YOLOv11 model demonstrates notable improvements in performance, with a precision increase from 90.9% to 93.1%, and a recall rate improvement from 77.0% to 84.6%. Furthermore, it shows a 4.6% rise in mAP50, from 84.0% to 88.6%. When compared to earlier YOLO versions such as YOLOv7, YOLOv8, and YOLOv9, the improved YOLOv11 achieves a significantly higher precision of 89.3% in resistor detection, surpassing YOLOv7’s 54.3% and YOLOv9’s 88.0%. In detecting defects like LED lights and capacitors, the improved YOLOv11 reaches mAP50 values of 77.8% and 85.3%, respectively, both outperforming the other models. Additionally, in the generalization tests conducted on the PKU-Market-PCB dataset, the model’s detection accuracy improved from 91.4% to 94.6%, recall from 82.2% to 91.2%, and mAP50 from 91.8% to 95.4%.These findings emphasize that the proposed YOLOv11 model successfully tackles the challenges of detecting small defects in complex backgrounds and across varying scales. It significantly enhances detection accuracy, recall, and generalization ability, offering a dependable automated solution for defect detection in electronic product manufacturing.

Carbon fiber prepreg is the key material for preparing carbon fiber composites, and its surface defects directly affect the performance of composites. Existing supervised learning defect detection methods require predefined defect types and rely on a large number of defect labeled samples, making it difficult to adapt to the challenges of unknown defects due to changes in the production environment. An unsupervised anomaly detection method for surface defects of carbon fiber prepregs is developed and the corresponding carbon fiber prepreg surface anomaly detection model (CFP-AD) is constructed in this paper, which only needs to use merely non-defective images for training. Firstly, the various features taken from VGG19 are fed into the designed multi-branch dimensional cross-refinement feature fusion module, which enhances the representation ability of multi-scale convolutional feature. Secondly, a convolutional autoencoder with residual pooling is designed to efficiently realize feature reconstruction, improving the reconstruction ability of the CFP-AD model for the edges of carbon fiber prepregs. Lastly, a context semantic similarity measurement function is introduced based on the l 2 norm anomaly score measurement to make the CFP-AD model more effective in highlighting defective regions. Extensive experimental results indicate that the CFP-AD model is able to accurately identify the image anomaly and locate defective regions in carbon fiber prepregs.

Various methods of detecting surface defects are being used in automated industrial manufacturing environments. This work presents the design and development of a laser charged couple device (CCD) displacement scanning system. The surface defect detection method using a laser CCD displacement sensor is derived from an idea in which surface defects such as cracks, inclusions and holes of three‐dimensional morphology characteristics are compared to normal surface especially in continuous casting slabs. Some novel research methods have been applied to develop the surface defect detection system used: first, getting a one‐dimensional distance matrix along transverse direction and a two‐dimensional matrix combining with a certain moving speed through the laser CCD displacement sensor; second, obtaining the slab surface profile and mapping to a greyscale image and finally, obtaining the surface defect regions by an image processing and searching algorithm and quantitatively detecting slab surface defect shape and depth. The research results in the lab trials have shown that the methodology proposed is effective to detect two‐dimensional defect size and reconstruct a three‐dimensional surface defect shape. At the same time, it is also able to accurately locate and identify surface defects and realise automatic surface defect non‐destructive detection online, and provides a theoretical base and technology idea for further studying surface defects online inspection for hot continuous casting slab.

Carbon fiber reinforced polymer (CFRP) has become an essential structural material in aerospace, new energy, rail transportation, and other fields due to its lightweight and high-strength properties. However, its complex manufacturing processes and demanding service environments can lead to various types of damage on both the surface and inside the material, significantly compromising safety. Existing detection techniques often adopt a step-by-step inspection mode, which results in complex processes and makes it difficult to achieve cross-dimensional correlation analysis of defects, leading to blind spots in structural health assessment. To address these issues, this paper proposes a method for simultaneous detection of surface and subsurface defects in CFRP based on infrared polarization imaging. First, we developed an infrared polarization imaging-based defect detection system and collected a CFRP defect dataset of samples containing various surface and subsurface defects. Then, we proposed a surface and subsurface defect detection network, which integrates extracted thermal diffusion features and polarization features to decouple and enhance surface and subsurface features. Through the constraints of multi-task defect detection, simultaneous detection of surface and subsurface defects is achieved. The experiments on the collected CFRP defect dataset demonstrate the effectiveness of the proposed method.

Aim at the problem of near surface blind area in ultrasonic TOFD technique, a new testing mode combined to ultrasound time of flight diffraction-reflection(TOFDR) and the three-fold reflected(TOFDW) based on secondary longitudinal waves was put forward. Through analysis the transmission characteristics of TOFDR and TOFDW, the two modes of the detection principle were illustrated, and discussed the ability in near surface detection of the two methods. Finally through the detecting artificial defects detection, the signal and image characteristics and detection sensitivity under the modified detection mode were studied. The artificial defect with buried depth of 1mm can be effectively detected from the D-scan image by the combined TOFDR and TOFDW methods.

Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet

In the industrial production of expanded thermoplastic polyurethane (E-TPU) midsoles, the surface defects still rely on manual inspection at present, and the eligibility criteria are uneven. Therefore, this paper proposes an E-TPU midsole surface defect detection method based on machine vision to achieve automatic detection and defect classification. The proposed method is divided into three parts: image preprocessing, block defect detection, and linear defect detection. Image preprocessing uses RGB three channel self-inspection to identify scorch and color pollution. Block defect detection uses superpixel segmentation and background prior mining to determine holes, impurities, and dirt. Linear defect detection uses Gabor filter and Hough transform to detect indentation and convex marks. After image preprocessing, block defect detection and linear defect detection are simultaneously performed by parallel computing. The false positive rate (FPR) of the proposed method in this paper is 8.3%, the false negatives rate (FNR) of the hole is 4.7%, the FNR of indentation is 2.1%, and the running time does not exceed 1.6 s. The test results show that this method can quickly and accurately detect various defects in the E-TPU midsole.

Sanitary ceramic products, such as toilet and wash basin, are widely used in our daily life. Sanitary ceramics are expected to have some excellent physical properties, such as corrosion resistance, easy cleaning, and low water absorption. However, surface defects in sanitary ceramics are inevitable due to complex production processes and changing production environment. Therefore, surface defect detection must be performed in the manufacturing process of sanitary ceramics. There are many types of surface defects in sanitary ceramics, and different types of defects have large differences in characteristics and scales. Traditional detection methods with artificially designed features and classifiers are difficult to apply in this context. In addition, there are few studies on surface defect detection methods of sanitary ceramics based on deep neural networks. In this article, a lightweight real-time defect detection network based on the lightweight backbone MobileNetV3 is presented. The proposed network achieves multi-scale detection of surface defects in sanitary ceramics with a multi-layer feature pyramid. Combining region proposal network and anchor-free method, real-time defect detection is achieved. Finally, a detection head with channel attention structure and a low-level mixed feature classification strategy is used to perform defect classification with higher accuracy. Experimental results show that the proposed approach achieves at least 22.9% detection speed improvement and 35.0% average precision improvement while reducing memory consumption by at least 8.4% compared with the classic one-stage SSD, YOLO V3 and two-stage Faster R-CNN methods.

As a classical research direction in the field of computer vision, anomaly detection has a very wide range of application prospects. Surface defect detection is an extremely important part of the industrial production process. Most of the traditional defect detection solutions are based on machine vision or supervised learning paradigms. These methods require the use of a large number of supervised defect samples, but it is difficult to obtain sufficient defect data in the production process. Due to various random factors, it is not easy to identify all types of defects during the inspection phase. With the development of deep unsupervised learning in recent years, autoencoder or generative adversarial networks are widely used in surface defect detection problem, which rely solely on the modeling of good data to discriminate abnormal defects. Therefore, in this paper, we propose a neural architecture search-based defect detection method. We combine neural architecture search and generative adversarial learning to automate the search of encoder-decoder structures with a gradient optimization-based search method. The method learns a pair of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. Neural architecture search helps to search for a task-appropriate network architecture with better performance. Finally, the proposed method is evaluated on the industrial inspection dataset MVTec AD, and the effectiveness of the model detection is measured by AUROC metrics, demonstrating the validity of the methods is demonstrated by both quantitative and qualitative analysis.

Aiming at the shortcomings of manual detection methods for surface defects of bearing outer rings, a surface defect detection method based on machine vision is proposed. The common defects on the bearing surface are scratches, cracks, and peeling. The defect images are analyzed by the LabVIEW image processing module. Make fast and accurate inspections. In this paper, a CMOS industrial camera is used to obtain the bearing image, and the gray histogram of the image is analyzed to determine whether it is a defective bearing. In order to solve the problem of uneven illumination in image segmentation, it is proposed to first use morphological processing for background. Then, the difference map containing defect information is obtained, and then the Otsu method is used for image segmentation; an edge detection method is designed based on morphology, which can achieve complete edge extraction and good denoising effect. Experiments show that the method has strong practicability and self-adaptability, and can accurately detect bearing surface defects.