Automatic Visual Inspection Method Research Articles

Deep learning-based visual ensemble method for high-speed railway catenary clevis fracture detection

This paper proposes an automatic visual inspection method for the fracture detection of clevises in the catenary systems of high-speed railways, using images of catenaries captured by an inspection vehicle. First, the clevises are extracted from the catenary image using a convolutional neural network based algorithm, known as the faster region-based convolutional neural network. Because the structure of catenary systems does not have many variations and the contextual information near a catenary fitting may have strong correlation with its category, the architecture of the original faster region-based convolutional neural network is modified to make use of the contextual information of the regions of interest in the images for object recognition. A crack detection process is then used to recognize the fractures of clevises. To detect the cracks, the edge map of the clevis sub-image is generated using a region-scalable fitting model. Areas where the cracks are most likely to occur are projected from a standard clevis image to the clevis sub-image by shape context matching and affine transformation matrix computation. The cracks are then recognized by calculating the wavelet entropy inside these areas followed by morphological filtering. Experimental results show that the modified faster region-based convolutional neural network architecture achieves better results in clevis extraction than the original architecture as well as some other state-of-art object detection models. The detection is not affected by the scaling, texture and grayscale changes of the clevises caused by the variation of shooting distance, shooting angle and illumination variations. The fractures of the clevises can be accurately and reliably detected using the fracture detection method proposed in this paper and the performance of this visual inspection method meets the strict requirements for catenary system maintenance.

Automatic visual inspection method for a coated surface having an orange peel effect

In many production processes, defect detection has played an important role in preventing the outflow of defective products. As such, there are still many production lines that rely on an inspector's visual observation. In this study, using fluorescent tubes as an irradiation light source, we propose a simple and effective visual inspection method for detecting convex defect on painted surfaces having an orange peel effect. The proposed method detects and distinguishes painted surface defects in an area illuminated by fluorescent tubes, using the combination of local segmented results and gradient information computed with different differential operations. The inspection device for detecting the defect can be easily installed in the production line without changing the production line, and can be constructed at a low cost without using additional adaptations. The performance of the proposed method was evaluated with the data of real products captured with an inspection device installed on the production line. Experimental results show that this method can provide a better detection in the inspection region with a low false‐detection rate. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.

Automatic visual inspection of a missing split pin in the China railway high-speed.

The split pin (SP) on the caliper brake is a vital component of the brake system of a bogie traveling along the China railway high-speed (CRH), and the absence of the SP could cause serious train accidents. A new automatic visual inspection method is proposed for the quick and accurate detection of SP faults of the CRH. The proposed approach is based on the histogram of gradient (HOG) combined with the complete local binary pattern (CLBP). First, a fast pyramid template matching technique is presented for localizing the region of interest to reduce the searching scope. Under the multiresolution pyramid model for target localization, a coarse-to-fine strategy is employed to ensure that the recognizing speed of the SP for the entire image is increased significantly. Second, a hierarchical framework is adopted at the localizing and inspecting stages of the SP to automatically implement the inspection tasks. To increase the robustness to the outside complex illumination, the HOG feature for localizing the target and the CLBP feature for examining the state of the SP (i.e., missing or not-missing) are extracted in the Sobel gradient domain. The localization and recognition stages are both fulfilled through the use of their respective intersection kernel support vector machine classifiers and corresponding features. In conclusion, experimental results indicate that the inspection system achieves a high accuracy rate of more than 99.0% and a real-time speed, thus proving that the proposed method is effective for the fault inspection of the SP and can satisfy the requirements of CRH's actual application.

Machine learning-based imaging system for surface defect inspection

Modern inspection systems based on smart sensor technology like image processing and machine vision have been widely spread into several fields of industry such as process control, manufacturing, and robotics applications in factories. Machine learning for smart sensors is a key element for the visual inspection of parts on a product line that has been manually inspected by people. This paper proposes a method for automatic visual inspection of dirties, scratches, burrs, and wears on surface parts. Imaging analysis with CNN (Convolution Neural Network) of training samples is applied to confirm the defect’s existence in the target region of an image. In this paper, we have built and tested several types of deep networks of different depths and layer nodes to select adequate structure for surface defect inspection. A single CNN based network is enough to test several types of defects on textured and non-textured surfaces while conventional machine learning methods are separately applied according to type of each surface. Experiments for surface defects in real images prove the possibility for use of imaging sensors for detection of different types of defects. In terms of energy saving, the experiment result shows that proposed method has several advantages in time and cost saving and shows higher performance than traditional manpower inspection system.

Research on automatic visual inspection method and system for foreign sub-stances in medicine transfusion liquid

Research on automatic visual inspection method and system for foreign sub-stances in medicine transfusion liquid

A Proposal of a Visual Inspection Method for Detecting Low Contrast Defects of an IC Lead Frame by Using a Spline Function

An automatic visual inspection method for an IC lead frame, which is effective to detect low contrast defects called stains and irregular luster, is desired. Low contrast defects are usually difficult to be distinguished clearly from the normal area by the difference of intensity level. Therefore, a simple subtraction technique using a good product as a reference image is not effective for detecting low contrast defects. This paper proposes a novel automatic visual inspection method effective to detect low contrast defects by using a spline function. In our method, a virtual good-product image is created for each product under the visual inspection by using a spline function. And the intensity subtraction technique is applied between a target image and a virtual good-product image. The intensity deviation of the virtual good-product image from the normal one can be made small enough to distinguish low contrast defects. Thus, the proposed method realizes an effective detection of low contrast defects and the reduction of false detection for the good product.

An automatic visual inspection method for a plastic surface based on image partitioning and gray‐level histograms

AbstractThe outlook examination in fabrication of plastic products in the past has been performed mainly by visual inspection. The automation of the procedure is now considered to be important. This paper discusses a simple automatic detection method for defects on the surface of plastic electronic components such as integrated circuits. As the first step, optical properties of the defect such as flaw, pin‐hole and bulge on the surface of the plastic components are discussed. This indicates problems in the detection of those defects. Then the illumination method using a planar light source is discussed to obtain a stable image against surface inclination. The procedure for practical detection of defects is developed in this paper as follows. First, to separate the image of the component surface from the background, the projection distribution of the gray‐level histograms is constructed. The boundary of the component surface is extracted based on the histogram. Then the surface image is partitioned into subareas. The gray‐level histogram is constructed for each partitioned subarea. Based on the maximum and the most‐frequent value of the gray‐level histogram, the existence or nonexistence of a defect in the subarea is determined. The experiment for the defect detection was performed using typical plastic components which are determined good or no‐good. It is demonstrated that a correct defect detection can be performed for all kinds of components.

Pattern Recognition for Automatic Visual Inspection

Three major approaches to pattern recognition, (1) template matching, (2) decision-theoretic approach, and (3) structural and syntactic approach, are briefly introduced. The application of these approaches to automatic visual inspection of manufactured products are then reviewed. A more general method for automatic visual inspection of IC chips is then proposed. Several practical examples are included for illustration.© (1982) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.