Narrow Results

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.

The rapid development of computer vision techniques has brought new opportunities for manufacturing industries, accelerating the intelligence of manufacturing systems in terms of product quality assurance, automatic assembly, and industrial robot control. In the electronics manufacturing industry, intensive variability in component shapes and colors, background brightness, and visual contrast between components and background results in difficulties in printed circuit board image classification. In this paper, we apply computer vision techniques to detect diverse electronic components from their background images, which is a challenging problem in electronics manufacturing industries because there are multiple types of components mounted on the same printed circuit board. Specifically, a 13-layer convolutional neural network (ECON) is proposed to detect electronic components either of a single category or of diverse categories. The proposed network consists of five Convolution-MaxPooling blocks, followed by a flattened layer and two fully connected layers. An electronic component image dataset from a real manufacturing company is applied to compare the performance between ECON, Xception, VGG16, and VGG19. In this dataset, there are 11 categories of components as well as their background images. Results show that ECON has higher accuracy in both single-category and diverse component classification than the other networks.

While robot-assisted minimally invasive surgery (RMIS) procedures afford a variety of benefits over open surgery and manual laparoscopic operations (including increased tool dexterity, reduced patient pain, incision size, trauma and recovery time, and lower infection rates [1], lack of spatial awareness remains an issue. Typical laparoscopic imaging can lack sufficient depth cues and haptic feedback, if provided, rarely reflects realistic tissue–tool interactions. This work is part of a larger ongoing research effort to reconstruct 3D surfaces using multiple viewpoints in RMIS to increase visual perception. The manual placement and adjustment of multicamera systems in RMIS are nonideal and prone to error [2], and other autonomous approaches focus on tool tracking and do not consider reconstruction of the surgical scene [3,4,5]. The group’s previous work investigated a novel, context-aware autonomous camera positioning method [6], which incorporated both tool location and scene coverage for multiple camera viewpoint adjustments. In this paper, the authors expand upon this prior work by implementing a streamlined deep reinforcement learning approach between optimal viewpoints calculated using the prior method [6] which encourages discovery of otherwise unobserved and additional camera viewpoints. Combining the framework and robustness of the previous work with the efficiency and additional viewpoints of the augmentations presented here results in improved performance and scene coverage promising towards real-time implementation.

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.

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.

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.

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.

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.

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.

Intelligent agriculture has become the development trend of agriculture in the future, and it has a wide range of research and application scenarios. Using machine learning to complete basic tasks for people has become a reality, and this ability is also used in machine vision. In order to save the time in the fruit picking process and reduce the cost of labor, the robot is used to achieve the automatic picking in the orchard environment. Cherry target detection algorithms based on deep learning are proposed to identify and pick cherries. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, we propose a cherry detection model based on YOLOv5s. First, the shallow feature information is enhanced by convolving the feature maps sampled by two times down in BackBone layer of the original network model to the input end of the second and third CSP modules. In addition, the depth of CSP module is adjusted and RFB module is added in feature extraction stage to enhance feature extraction capability. Finally, Soft- Non-Maximum Suppression (Soft-NMS) is used to minimize the target loss caused by occlusion. We test the performance of the model, and the results show that the improved YOLOv5s-cherry model has the best detection performance for small and dense cherry detection, which is conducive to intelligent picking.

Detecting collision-course targets in aerial scenes from purely passive optical images is challenging for a vision-based sense-and-avoid (SAA) system. Proposed herein is a processing pipeline for detecting and evaluating collision course targets from airborne imagery using machine vision techniques. The evaluation of eight feature detectors and three spatio-temporal visual cues is presented. Performance metrics for comparing feature detectors include the percentage of detected targets (PDT), percentage of false positives (POT) and the range at earliest detection ($R_{\mathrm{\text{det}}}$). Contrast and motion-based visual cues are evaluated against standard models and expected spatio-temporal behavior. The analysis is conducted on a multi-year database of captured imagery from actual airborne collision course flights flown at the National Research Council of Canada. Datasets from two different intruder aircraft, a Bell 206 rotor-craft and a Harvard Mark IV trainer fixed-wing aircraft, were compared for accuracy and robustness. Results indicate that the features from accelerated segment test (FAST) feature detector shows the most promise as it maximizes the range at earliest detection and minimizes false positives. Temporal trends from visual cues analyzed on the same datasets are indicative of collision-course behavior. Robustness of the cues was established across collision geometry, intruder aircraft types, illumination conditions, seasonal environmental variations and scene clutter.

In traditional conveyor belt edge detection methods, contact detection methods have a high cost. At the same time noncontact detection methods have low precision, and the methods based on the convolutional neural network are limited by the local operation features of the convolution operation itself, causing problems such as insufficient perception of long-distance and global information. In order to solve the above problems, a dual flow transformer network (DFTNet) integrating global and local information is proposed for belt edge detection. DFTNet could improve belt edge detection accuracy and suppress the interference of belt image noise. In this paper, the authors have merged the advantages of the traditional convolutional neural network’s ability to extract local features and the transformer structure’s ability to perceive global and long-distance information. Here, the fusion block is designed as a dual flow encoder–decoder structure, which could better integrate global context information and avoid the disadvantages of a transformer structure pretrained on large datasets. Besides, the structure of the fusion block is designed to be flexible and adjustable. After sufficient experiments on the conveyor belt dataset, the comparative results show that DFTNet can effectively balance accuracy and efficiency and has the best overall performance on belt edge detection tasks, outperforming full convolution methods. The processing image frame rate reaches 53.07 fps, which can meet the real-time requirements of the industry. At the same time, DFTNet can deal with belt edge detection problems in various scenarios, which gives it great practical value.

The body movement is one of the most important factors to evaluate the sleep quality. In general, the sleep motion is hardly investigated, and it must take a long time to observe the motion of the patient in terms of a pre-recoded video storage media with high speed playing. This paper proposes an image-based solution to recognize the sleep motions. We use the contact free and IR-based night vision camera to capture the video frames during the sleep of the patient. The video frames are used to recognize the body positions and the body directions such as the “body up”, “body down”, “body right”, and “body left”. In addition to the image processing, the proposed artificial neural network (ANN) sleep motion recognition solution is composed of two neural networks. These two neural networks are organized as in a cascade configuration. The first ANN model is used to identify the body position features from the images; and the follower ANN model is constructed based on the features that are identified by the first ANN model to recognize the body direction. Finally, the implementations and the practical results of this work are all illustrated in this paper.

Due to their advantages, omni-directional mobile robots have found many applications especially in robotic soccer competitions. However, omni directional navigation system, omni-vision system and kicking mechanism in such mobile robots have not ever been combined. This situation brings the idea of a robot with no head direction into existence, a comprehensive omni directional mobile robot. Such a robot can respond more quickly and it would be capable for more sophisticated behaviors with multi-sensor data fusion algorithm for global localization. Despite recent advances, effective control and self-localization methods of omni-directional mobile robots remain as important and challenging issues. For this purpose, we utilize the sensor data fusion method in the control system parameters, self localization and world modeling. A vision-based self-localization and the conventional odometry systems are fused for robust self-localization, The methods have been tested in the many Robocup competition field middle size robots. The localization algorithm includes filtering, sharing and integration of the data for different types of objects recognized in the environment. This paper has tried to focus on description of areas such as omni directional mechanisms, mechanical structure, omni-vision sensor for object detection, robot path planning, and other subjects related to mobile robot's software.

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$\%$.

The use of machine vision technology is being investigated at VTT for improving the colour quality and productivity of web offset printing. The visual inspection of colour quality is performed by a colour CCD camera which traverses the moving web under a stroboscopic light. The measuring locations and goal values for the colour register, the ink density and the grey balance are automatically determined from the PostScript™ description of the digital page. A set of criteria is used to find the most suitable spots for the measurements. In addition to providing data for on-line control, the page analysis estimates the zone wise link consumption of the printing plates as a basis for presetting the ink feed. Target calorimetric CIE-values for grey balance and critical colours are determined from the image originals. The on-line measurement results and their derivations from the target values are displayed in an integrated manner. The paper gives test results of computation times, measurements of register error with and without test targets and the colour measuring capabilities of the system. The results show that machine vision can be used for on-line inspection of colour print quality. This makes it possible to upgrade older printing presses to produce a colour quality that is competitive with more modern presses.

In this paper, a new machine vision algorithm for close-range position sensing and bin picking is presented where a Hopfield Neural Network (HNN) is used for the stereo matching process. Stereo Matching is formulated as an energy minimization task and this minimization is accomplished using the HNNs. Various other important aspects of this Vision System are discussed including camera calibration and objects localization using a clustering algorithm.

To promote the accuracy and degree of automation in the process of oil measuring, an automatic detection system based on machine vision has been established. In this system, CCD camera is treated as a sensor and it collects image of oil level of the measurement cylinder. After image preprocessing, we improve the Canny margin extraction to achieve a better effect on margin detection. And then we use least-squares fitting to detect the sub-pixels margin line of the image. Finally, we calculate the oil volume.