MCAM-Net: multi-scale convolutional attention for enhanced industrial surface defect detection

The detection of rail surface defects is very important in railway transportation. However, the edge defects on both sides of the rail and the multi-scale variation between different types of defects both pose challenges to the detection of rail surface defects. In order to solve the above problems, this paper proposes a novel rail surface defect detection network, YOLOv5s-VF. First, we design a sharpening functional attention mechanism (V-CBAM) that contains two key components: adaptive channel attention (F-CAM) and sharpened spatial attention (SSA). In F-CAM, we use one-dimensional convolution with adaptive convolution kernels for cross-channel connections, which reduces the number of parameters of the attention mechanism without affecting its performance. In SSA, we design a sharpening filter suitable for spatial attention, which is used to enhance the attention to the edge position defects of railway tracks and enhance the detection effect of the network on edge defects. Second, we construct a microscale adaptive spatial feature fusion (M-ASFF), which adds a high-resolution feature extraction layer to enhance the details of the underlying features of tiny defects. At the same time, in order to prevent the loss of detailed information and the excessive increase of the parameters of the model, the low-resolution feature layer is removed. Combined with adaptive spatial feature fusion, it can prevent the semantic conflict caused by the fusion of features at different scales. Finally, given the lack of labeled public rail surface defect datasets, this paper is based on the collection of real rail images and manually labels defects to train an object detection network and open source it. The experimental results show that YOLOv5s-VF outperforms the existing rail surface defect detection methods with a detection accuracy of 93.5% and a detection speed of 114.9 fps.

Some uncontrollable defects will occur on the surface of metal workpieces during processing. The existence of surface defects not only affects the appearance of the finished product, but also affects the quality to a certain extent. Surface defect detection of metal workpieces can effectively improve product quality and production efficiency, and is an important link in the process of product quality control. Although there are many different types of surface defect detection methods, in the actual production process, due to the characteristics of multiple types and irregular distribution of the surface defects of metal workpieces, in most cases, manual inspection or simple machine inspection is still used to detect the surface of metal workpieces. Defect inspections often lead to missed inspections and false inspections. The defect detection efficiency, accuracy and precision of metal workpieces still need to be further improved. This paper studies the method of detecting the surface defects of metal workpieces based on deep learning, provides the surface defect recognition accuracy and defect detection rate of metal workpieces, and provides references for the staff and scientific researchers engaged in metal workpiece defect detection.

For decades, aluminum extrusion has been successfully applied in the manufacturing of profiles for the applications ranging from locomotives to skyscrapers. In recent years however, increasing profile complexity and the need for rapid production have lead to greater challenges for manufactures seeking rapid and robust production procedures. As a consequence, the occurrence of defects in extruded profile surfaces continues to create difficulties often requiring disposal of entire components. Hence, quality inspection of the profiles must be performed prior to packing in order to identify and appropriately manage defect-containing extrusions. Up until now, quality control in extrusion factories is primarily performed by the human eye due to its high performance in discriminating defect varieties. But human performance is cost intensive and furthermore prone to failure, especially when applied in high-throughput environments. On that account this paper proposes an approach in surface defect classification and detection, whereby a simple camera records the extruded profiles during production and a neural network architecture distinguishes between immaculate surfaces and surfaces containing a variety of common defects (surface defect classification). Furthermore, a neural network is employed to point out the defects in the video frames (surface defect detection). In this work, we show that methods from artificial intelligence are highly compatible with industrial applications such as quality control even under common industry constraints such as very limited data set sizes for training a neural network. Data augmentation as well as transfer learning are the key ingredients for training networks that meet the high requirements of modern production facilities in detecting surface defects, particularly when access to training sets is limited. Accuracies of 0.98 in the classification and mean average precisions of 0.47 in the detection setting are achieved whilst training on a data set containing as little as 813 images. Real-time classification and detection codes are implemented, and the networks perform reliably despite changes in lighting conditions and camera orientation.

To solve the problem of false defect detection owing to the interference of the texture attribute of ceramic tiles, a method for detecting surface defects in complex-textured ceramic tiles is proposed. Based on the visual detection principle of ceramic tile surfaces, an image acquisition system is established to obtain the ceramic tile image. After image segmentation and correction, the surface defects are preliminarily detected using a saliency detection method. Then, the image sub-block containing the defect area is cut out for secondary detection. The false defects are eliminated, and the final detection of the ceramic tile surface defects is completed using the defect determination method. The feasibility and effectiveness of the defect detection method are studied via comparative experiments. Experimental results show that the maximum accuracy rate of the proposed defect detection method is 98.75%, which satisfies the actual detection requirements and confirms the practical significance of the proposed method.

Attention-based convolution neural network for magnetic tile surface defect classification and detection

Aiming at the problems of uneven light reflectivity on the spherical surface and high similarity between the stems/calyxes and scars that exist in the detection of surface defects in apples, this paper proposed a defect detection method based on image segmentation and stem/calyx recognition to realize the detection and recognition of surface defects in apples. Preliminary defect segmentation results were obtained by eliminating the interference of light reflection inhomogeneity through adaptive bilateral filtering-based single-scale Retinex (SSR) luminance correction and using adaptive gamma correction to enhance the Retinex reflective layer, and later segmenting the Retinex reflective layer by using a region-growing algorithm. The texture features of apple surface defects under different image processing methods were analyzed based on the gray level co-occurrence matrix, and a support vector machine was introduced for binary classification to differentiate between stems/calyxes and scars. Deploying the proposed defect detection method into the embedded device OpenMV4H7Plus, the accuracy of stem/calyx recognition reached 93.7%, and the accuracy of scar detection reached 94.2%. It has conclusively been shown that the proposed defect detection method can effectively detect apple surface defects in the presence of uneven light reflectivity and stem/calyx interference.

Surface inspection and defect detection are essential procedures in the quality control of mass production. It is challenging to identify curvilinear defects on a complex background. This paper proposes a novel and fast surface defect detection method based on improved Gabor filters. The introduced system firstly employs improved Gabor filter banks to generate corresponding energy maps. Hysteresis thresholding combined with region grouping and pruning is used to extract defect regions, integrate defect fragments and remove false positives. Our method exhibits excellent performance on two different kinds of surface defect dataset and thus proves to be a practical and accurate solution to surface defect detection.

Defect detection is an essential requirement for quality control in steel plate manufacturing. Traditional defect detection methods use the classic image process, high labor cost, and inefficient detection ability. This paper proposes a novel image detection model for steel plate surface defect detection called ID-RCNN. This model builds a new network based on Dynamic RCNN. In detail, we use a ResNet50 with an attention module as the backbone for feature extraction to better detect surface defects in steel plate production. Then, we proposed a novel attention module called CM, which is improved for Convolutional Block Attention Module (CBAM). The experimental results show that this model is more suitable for use in production than other steel plate surface defect detection methods, with the mean Average Precision of defect detection with 99.1% accuracy. We have tested in the DAGM defects dataset, and experiments have shown that this model is equally valid. This research can greatly improve the ability of Deep Learning models in industrial defect detection.

The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection technologies. This article presents the construction of a non-coaxial differential eddy current sensor (Tx-Rx sensor) and examines the detection of surface defects in a small diameter bar. A COMSOL 3D model is developed to examine the variations in eddy current distribution and defect signal characteristics between the plate and rod components. The position of the excitation coil on the bar and the eddy current disruption around the defect are examined. Additionally, a Tx-Rx sensor has been developed and enhanced concerning coil dimensions, coil separation, and elevation height. An experimental system is established to detect bar structures with surface defects of varying depths, and a model correlating differential signal attenuation with defect depth is proposed, achieving a quantitative relative error of less than 5%, thereby offering a reference for the quantitative detection of bar surface defects.

For monitoring the conditions of railway infrastructures, axle box acceleration (ABA) measurements on board of trains is used. In this paper, the focus is on the early detection of short surface defects called squats. Different classes of squats are classified based on the response in the frequency domain of the ABA signal, using the wavelet power spectrum. For the investigated Dutch tracks, the power spectrum in the frequencies between 1060-1160Hz and around 300Hz indicate existence of a squat and also provide information of whether a squat is light, moderate or severe. The detection procedure is then validated relying on real-life measurements of ABA signals from measuring trains, and data of severity and location of squats obtained via a visual inspection of the tracks. Based on the real-life tests in the Netherlands, the hit rate of the system for light squats is higher than 78%, with a false alarm rate of 15%. In the case of severe squats the hit rate was 100% and zero false alarms.

The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP-Add module is added after the Concat module in the network's Head section to strengthen the network's ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model's CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. Moreover, when compared to other novel algorithms, our improved model demonstrated enhanced detection accuracy and significantly improved detection speed. These results collectively indicate that our proposed enhanced model effectively fulfills the industry's demand for rapid and efficient detection and recognition of metal surface defects.

Surface defect detection is crucial in maintaining product quality across various industries. Traditional manual inspection methods are often time-consuming and subjective, which can result in inaccuracies and higher production costs. With the use of deep learning techniques, significant advancements have been made in automating the process of surface defect detection in recent years. Moreover, deep learning includes a variety of techniques, and image recognition-based deep learning is especially relevant to our field of study, which is the main focus of this research paper.In the industrial surface defect detection field, researchers have always aimed to create a deep learning-based intelligent defect detection system that achieves near-zero defect rates while maintaining a lightweight, efficient, and cost-effective solution. However, these objectives often conflict with each other, and it is unrealistic to develop a model that can achieve all of them simultaneously. Some trade-offs must be made. If accuracy is the top priority, a large amount of defective data labeled for supervised learning is usually required. If lightweight and low cost is prioritized, a simple small model such as Auto-Encoder is usually used, along with a large number of flawless images for unsupervised learning to minimize the cost of labeling.As mentioned before, it is very difficult to design a single model that can achieve all of them simultaneously. However, present-day studies frequently center on accomplishing those tasks using a single model and rarely address the multi-model architecture. This paper presents a Surface Defect Detection and Classification System that builds on the current state-of-the-art model in the field of surface defect detection, along with the zero-shot learning (ZSL) classifier based on VAEGAN and the Variational Auto-Encoder developed by our laboratory.We have developed a Surface Defect Detection and Classification System that effectively integrates the aforementioned three models. It has been validated on a dataset of metal surface defects, yielding excellent results. This system not only achieves defect detection rates that comply with industrial standards and low false positive rates but also maintains characteristics such as lightweight, low latency, and low annotation cost. In addition to achieving the above goals, this system can also instantly identify and issue anomaly notifications when there are unseen anomalies, which is generally impossible to do with supervised learning anomaly detection models.

Research progress in deep learning for ceramics surface defect detection

In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.

Surface defect detection for the printed circuit board (PCB) is essential in PCB manufacturing. Existing defect detection networks have several problems: low detection efficiency, high memory consumption, and low sensitivity to small defects. To address these issues, we propose a new lightweight deep-learning-based defect detection network, YOLOX with a modified CSPDarknet and coordinate attention (YOLOX-MC-CA). YOLOX-MC-CA is developed on the YOLOX and uses the coordinate attention (CA) mechanism to improve the recognition capability of small PCB surface defects. The backbone network in YOLOX is also modified into a new CSPdarknet structure with some inverted residual blocks. The modified CSPDarknet (MC) backbone network helps the YOLOX decrease the number of parameters on the premise of guaranteeing the feature extraction ability. We evaluated the YOLOX-MC-CA with an augmented dataset based on a public PCB surface defect dataset. Compared to the squeeze-and-excitation (SE) module, convolutional block attention module (CBAM), and other approaches in previous research, the CA mechanism improves the network with more detection precision for the small PCB surface defects. The experimental results demonstrate that our network is superior to other state-of-the-art (SOTA) networks for PCB surface defect detection, scoring 99.13% on mean average precision (mAP) and 47.6 frames per second (FPS) on detection speed, only occupying a parameter space of 3.79 million (M). It demonstrates that the proposed network is more suitable for deployment on embedded systems.