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Machine vision assessment methodology has become increasingly appealing for manufacturing automation due to innovations in noninvasive technologies such as eddy current and ultrasonic testing, which have enhanced the circumstances for bearing defect identification. At this point, manual detection results in low lifespans and reliability. So, we present an innovative rider optimization-driven mutated convolutional neural network (RO-MCNN) technique for surface defect detection of bearings based on machine vision. To evaluate the effectiveness of the suggested approach, samples of the bearing surface with various defects were gathered. The raw data specimens are denoised using a Gaussian filter, and the defect-oriented surface patterns are then extracted using a local binary pattern (LBP) technique. Subsequently, the MCNN model is designed to identify and categorize the various sorts of defects. Experimental results obtained high accuracy (99.0%), F1-score (98.7%), recall (98.6%) and precision (98.5%), which validate the greater of RO-MCNN over existing methods, demonstrating its capability in robustly detecting and classifying bearing defects with high precision and reliability, thereby advancing the efficacy of machine vision in industrial defect assessment. The MCNN model’s performance is improved and the loss function is decreased by using the RO method. The results of the experiments showed that the suggested RO-MCNN technique outperforms current strategies in terms of bearing defect type detection and classification.

As industrialization accelerates, the risk and damage caused by fires in industrial settings have become increasingly severe. Current fire detection and response systems suffer from slow response times and inadequate accuracy, failing to meet the demands of modern industrial safety. This study presents the design and implementation of a real-time fire detection and response system based on machine vision. The system employs high-precision fire source recognition algorithms and intelligent control algorithms, utilizing cameras for real-time fire monitoring and deep learning techniques to accurately locate fire sources. Firefighting robots then promptly extinguish the identified fires. Experimental results demonstrate that the system achieves a fire source detection accuracy of up to 95% and an average response time of less than 3s in simulated industrial environments, significantly enhancing the intelligence and effectiveness of industrial fire protection. Furthermore, the system can automatically monitor and alert, transmitting fire information to relevant personnel in real-time, thereby providing robust technological support and assurance for industrial safety management. Moving forward, the research team will optimize existing algorithms and introduce new deep learning models to maintain high-efficiency fire detection performance in complex and dynamic industrial environments. Additionally, IoT integration and multi-sensor fusion will further enhance the system’s monitoring and response capabilities. We will also explore the application of the system in actual industrial sites and study its feasibility and scalability in other high-risk environments.

With the breakthrough development of technology in the 4.0 digitalization era, computer vision and deep learning have emerged as promising technologies for industrial quality inspection. By leveraging the power of machine learning algorithms, computer vision systems can automatically detect and classify defects in industrial products with high precision and efficiency. As the system processes more data and identifies more complicated defects, it can become more accurate and efficient in detecting imperfections and ensuring product quality. This paper proposes an inspection system integrated with the YOLOv8 network to assess the quality of products based on their surface. The data multi-threading mechanism is also applied in the system to ensure real-time processing operations. The experimental results show that the proposed system reaches high detection accuracy among different types of defects, at above 90%. Additionally, the proposed model reveals that the scratch defect is the most difficult error to detect, requiring a long time for decision analysis.

In the field of computer vision, camera calibration is a hot issue. For the existing coupled problem of calculating distortion center and the distortion factor in the process of camera calibration, this paper presents an iterative-decreasing calibration method based on regional circle, uses the local area of the circle plate to calculate the distortion center coordinates by iterative declining, and then uses the distortion center to calculate the local area calibration factors. Finally, makes distortion center and the distortion factor for the global optimization. The calibration results show that the proposed method has high calibration accuracy.

The application of machine vision to industrial robots is a hot topic in robot research nowadays. A welding robot with machine vision had been developed, which is convenient and flexible to reach the welding point with six degrees-of-freedom (DOF) manipulator, while the singularity of its movement trail is prevented, and the stability of the mechanism had been fully guaranteed. As the precise industry camera can capture the optical feature of the workpiece to reflect in the camera’s CCD lens, the workpiece is identified and located through a visual pattern recognition algorithm based on gray scale processing, on the gradient direction of edge pixel or on geometric element so that high-speed visual acquisition, image preprocessing, feature extraction and recognition, target location are integrated and hardware processing power is improved. Another task is to plan control strategy of control system, and the upper computer software is programmed in order that multi-axis motion trajectory is optimized and servo control is accomplished. Finally, prototype was developed and validation experiments show that the welding robot has high stability, high efficiency, high precision, even if welding joints are random and workpiece contour is irregular.

Localizing facial features is a critical component in many computer vision applications such as expression recognition, face recognition, face tracking, animation, and red-eye correction. Practical applications require detectors that operate reliably under a wide range of conditions, including variations in illumination, pose, ethnicity, gender and age. One challenge for the development of such detectors is the inherent trade-off between robustness and precision. Robust detectors tend to provide poor localization and detectors sensitive to small changes in local structure, which are needed for precise localization, generate a large number of false alarms. Here we present an approach to this trade-off based on context dependent inference. First, robust detectors are used to detect contexts in which target features occur, then precise detectors are trained to localize the features given the detected context. This paper describes the approach and presents a thorough empirical examination of the parameters needed to achieve practical levels of performance, including the size of the training database, size of the detector's receptive fields and methods for information integration. The approach operates in real time and achieves, to our knowledge, the most accurate localization performance to date.

In this paper, we address the problem of recognizing group activities of human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene to effectively handle noisy information. Two novel features, Group Interaction Energy (GIE) feature and Attraction and Repulsion Features, are proposed to better describe group activities within a GIZ. We demonstrate the performance of our method in two ways by (i) comparing the performance of the proposed method with the previous methods and (ii) analyzing the influence of the proposed features and GIZ-based meaningful group detection on group activity recognition using public datasets.

In many real-world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, either the features are given by common sense like edges or corners in image analysis, or they are directly determined by expertise. They mostly remain task-specific, although human may learn the life time, and use different features too, although same features do help in recognition. It seems that a universal feature set exists, but it is not yet systematically found. In our contribution, we try to find such a universal image feature set that is valuable for most image related tasks. We trained a shallow neural network for recognition of natural and non-natural object images before different backgrounds, including pure texture and handwritten digits, using a Shannon information-based algorithm and learning constraints. In this context, the goal was to extract those features that give the most valuable information for classification of the visual objects, hand-written digits and texture datasets by a one layer network and then classify them by a second layer. This will give a good start and performance for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract unique features which are valid in all three different kinds of tasks. They give classification results that are about as good as the results reported by the corresponding literature for the specialized systems, or even better ones.

Front vehicle detection technology is one of the hot spots in the advanced driver assistance system research field. This paper puts forward a method for front vehicles detection based on video-and-laser-information at night. First of all, video images and laser data are pre-processed with the region growing and threshold area expunction algorithm. Then, the features of front vehicles are extracted by use of a Gabor filter based on the uncertainty principle, and the distances to front vehicles are obtained through laser point cloud. Finally, front vehicles are automatically classified during identification with the improved sequential minimal optimization algorithm, which was based on the support vector machine (SVM) algorithm. According to the experiment results, the method proposed by this text is effective and it is reliable to identify vehicles in front of intelligent vehicles at night.

General machine vision algorithms are difficult to detect LCD sub-pixel level defects. By studying the LCD screen images, we found that the pixels in the LCD screen are regularly arranged. The spectrum distribution of LCD images, which is obtained by the Fourier transform, is relatively consistent. According to this feature, a method of sub-pixel defect detection based on notch filter and image registration is proposed. First, we take a defect-free template image to establish registration template and notch-filtering template; then we take the defect images for image registration with registration template, and solve the offset problem. After the notch-filter template filtering the background texture, the defect is more obvious; Finally the defects are obtained by the threshold segmentation method. The experiment results show that the proposed method can detect sub-pixel defects accurately and quickly.

Real-time and accurate measurement of coal quantity is the key to energy-saving and speed regulation of belt conveyor. The electronic belt scale and the nuclear scale are the commonly used methods for detecting coal quantity. However, the electronic belt scale uses contact measurement with low measurement accuracy and a large error range. Although nuclear detection methods have high accuracy, they have huge potential safety hazards due to radiation. Due to the above reasons, this paper presents a method of coal quantity detection and classification based on machine vision and deep learning. This method uses an industrial camera to collect the dynamic coal quantity images of the conveyor belt irradiated by the laser transmitter. After preprocessing, skeleton extraction, laser line thinning, disconnection connection, image fusion, and filling, the collected images are processed to obtain coal flow cross-sectional images. According to the cross-sectional area and the belt speed of the belt conveyor, the coal volume per unit time is obtained, and the dynamic coal quantity detection is realized. On this basis, in order to realize the dynamic classification of coal quantity, the coal flow cross-section images corresponding to different coal quantities are divided into coal type images to establish the coal quantity data set. Then, a Dense-VGG network for dynamic coal classification is established by the VGG16 network. After the network training is completed, the dynamic classification performance of the method is verified through the experimental platform. The experimental results show that the classification accuracy reaches 94.34%, and the processing time of a single frame image is 0.270s.

Stereo vision and 3D reconstruction technologies are increasingly concerned in many fields. Stereo matching algorithm is the core of stereo vision and also a technical difficulty. A novel method based on super pixels is mentioned in this paper to reduce the calculating amount and the time. Stereo images from University of Tsukuba are used to test our method. The proposed method spends only 1% of the time spent by the conventional method. Through a two-step super-pixel matching optimization, it takes 6.72 s to match a picture, which is 12.96% of the pre-optimization.

In order to detect the type of vehicle seat and the missing part of the spring hook, this paper proposes an improved RANSAC-SURF method. First, the image is filtered by a Gauss filter. Second, an improved RANSAC-SURF algorithm is used to detect the types of vehicle seats. Extract the feature points of vehicle seats. The feature points are matched according to the improved RANSAC-SURF algorithm. Third, the image distortion of the vehicle seat is corrected by the method of perspective transformation. Determine whether the seat’s spring hook is missing or not according to the absolute value of the gray difference between the image collected by the camera and the image of the normal installation. The experimental results show that the MSE of the Gauss filter under a 5 $*$ 5 template is 19.0753, and the PSNR is 35.3261, which is better than that of the mean filter and the median filter. The total matching logarithm of feature points and the number of intersection points are 188 and 18, respectively, in the improved RANSAC-SURF matching algorithm.

A common study area in anomaly identification is industrial images anomaly detection based on texture background. The interference of texture images and the minuteness of texture anomalies are the main reasons why many existing models fail to detect anomalies. We propose a strategy for anomaly detection that combines dictionary learning and normalizing flow based on the aforementioned questions. The two-stage anomaly detection approach that is already in use is enhanced by our method. In order to improve baseline method, this research adds normalizing flow in representation learning and combines deep learning and dictionary learning. Improved algorithms have exceeded 95$\%$ detection accuracy on all MVTec AD texture type data after experimental validation. It shows strong robustness. The baseline method’s detection accuracy for the Carpet data was 67.9$\%$. The paper was upgraded, raising the detection accuracy to 99.7$\%$.

During the production, transportation and storage of nonwoven fabric mask, there are many damages caused by human or nonhuman factors. Therefore, checking the defects of nonwoven fabric mask in a timely manner to ensure the reliability and integrity, which plays a positive role in the safe use of nonwoven fabric mask. At present, the wide application of machine vision technology provides a technical mean for the defect detection of nonwoven fabric mask. On the basis of the pre-treatment of the defect images, it can effectively simulate the contour fluctuation grading and gray value change of the defect images, which is helpful to realize the segmentation, classification and recognition of nonwoven fabric mask defect features. First, in order to accurately obtain the image information of the nonwoven fabric mask, the binocular vision calibration method of the defect detection system is discussed. On this basis, the defect detection mechanism of the nonwoven fabric mask is analyzed, and the model of image processing based on spatial domain and Hough transform is established, respectively. The original image of the nonwoven fabric mask is processed by region processing and edge extraction. Second, the defect detection algorithm of nonwoven fabric mask is established and the detection process is designed. Finally, a fast defect detection system for nonwoven fabric mask is designed, and the effectiveness of the detection method for nonwoven fabric mask is analyzed with an example. The results show that this detection method has positive engineering significance for improving the detection efficiency of defects in nonwoven fabric mask.

This study investigated the three-dimensional (3D) printing of tubular tissue, especially vascular tissue, using a self-developed 3D bioprinter platform and tubular tissue support frame system based on machine vision technology. A 3D printing quality inspection scheme for tubular tissue based on machine vision was proposed by combining the current advanced image acquisition sensor device and theoretical and experimental analysis to measure the printing area in real time. A quantitative relationship between the quality of the tissue profile and the angle and brightness of tissue printed by hydrogel was established by changing the process parameters. A mathematical model for the visual inspection of tissue contour quality was established to realize its visual inspection and evaluation. This method can monitor the quality status of the printing target in real time and provide a basis for improving the accuracy of 3D bioprinting of tubular tissue and shortening the printing time.

Aimed at the problems of complex industrial site environment, difficult identification of assembly features and low positioning accuracy, a new visual identification and positioning method is proposed, which can well identify and locate parts and products with different characteristics. First, combined filtering is used in preprocessing to repair low-quality images. Then, edge detection and contour finding are performed on the preprocessed image. Finally, the internal and external parameters of the camera are calculated through the camera calibration, and the acquired pixel coordinates are converted into world coordinates. The results show that the combined filtering algorithm has good noise reduction effect and can remove image surface noise, which improves the recognition accuracy for the identification and positioning of different feature parts by the subsequent contour search method, and the algorithm can meet the requirements of the actual positioning accuracy of the assembly.

This paper describes a new visual sensor system for the recognition of touching and overlapping workpieces, designed to meet the requirements of real applications in the factory. The system analyses the grey scale image and comprises two stages: a feature-extraction stage, which is designed mainly in special-purpose hardware, and a model-based analysis stage. The system works with edge-based geometrical primitives and surface features. The model comprises topographical and procedural information in order to control the image-analysis, which is based on the “heuristic-search” technique. The hardware and software design is modular. The system is working well in a wide range of environmental conditions and with different kinds of workpieces. A prototype version is completed and has proved its reliability and performance in several applications in the factory.

This paper presents the design and development of a real-time eye-in-hand stereo-vision system to aid robot guidance in a manufacturing environment. The stereo vision head comprises a novel camera arrangement with servo-vergence, focus, and aperture that continuously provides high-quality images to a dedicated image processing system and parallel processing array. The stereo head has four degrees of freedom but it relies on the robot end-effector for all remaining movement. This provides the robot with exploratory sensing abilities allowing it to undertake a wider variety of less constrained tasks. Unlike other stereo vision research heads, the overriding factor in the Surrey head has been a truly integrated engineering approach in an attempt to solve an extremely complex problem. The head is low cost, low weight, employs state-of-the-art motor technology, is highly controllable and occupies a small-sized envelope. Its intended applications include high-accuracy metrology, 3-D path following, object recognition and tracking, parts manipulation and component inspection for the manufacturing industry.

We have developed an algorithm for unsupervised adaptive classification based on a finite number of “prototype populations” with distinctly different feature distributions, each representing a typically different source population of the inspected products. Intermittently updated feature distributions, of samples collected from the currently classified products, are compared to the distributions of pre-stored prototype populations, and accordingly the system switches to the most appropriate classifier. The goal of our approach is similar to the objectives of the previously proposed “Decision Directed” adaptive classification algorithms but our solution is particularly suitable for automatic inspection and classification on a production line, when the inspected items may come from a finite number of distinctly different sources.

The recognition of prototype populations as well as the classification task proper may be implemented by conventional classifiers, however neural networks (NN) are advantageous in two respects: There is no need to develop separate mathematical models for each classifier because the NN does it automatically during the training stage. The parallel structure of NN has the potential for very fast classification in real time, if implemented by dedicated parallel hardware. This is particularly important for high speed automatic sorting on a production line.

The practical feasibility of the approach was demonstrated by two applied examples, wherein two prototype populations of apples are recognized and sorted by size and color derived by machine vision. Three “Boltzmann-Perceptron Networks” (BPN) were used, one to recognize the prototype populations, while switching between the other two, for optimally classifying apples into two size and color categories. It is shown that misclassifications by adaptive classification are reduced, in comparison to non-adaptive classification.