Machine Vision Research Articles

Objective Video Quality Assessment Method for Object Recognition Tasks

In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision rather than human perceptual quality metrics. We used advanced machine learning models and custom Video Quality Indicators to enhance the predictive accuracy of object recognition performance under various conditions. Our results indicate a model performance, achieving a mean square error (MSE) of 672.4 and a correlation coefficient of 0.77, which underscores the effectiveness of our approach in real-world scenarios. These findings highlight not only the robustness of our methodology but also its potential applicability in critical areas such as surveillance and telemedicine.

Measurability of quality characteristics identified in latent spaces of Generative AI Models

Deep Learning can learn complex properties from image datasets, which are difficult to model with traditional machine vision algorithms, inherently in the form of disentangled latent spaces. With latent spaces of Generative AI models, a feature extraction method to access these properties can be implemented. This work evaluates whether the learned properties can be measured in the latent space. Quantity and quantity-value scale properties and the measurability of the dimensional quality characteristic ‘filling degree’ using a linear calibration function are demonstrated for an industrial machine vision application. An uncertainty indicator between 0.4–0.9 mm is estimated for the latent space measurements.

Preparation and properties of micro-arc oxidation ceramic coatings using machine vision technology

In this paper, a machine vision system is combined with non-immersion micro-arc oxidation (MAO) technology, using color as a visual feature, and automatically determining the MAO process through a self-developed color image segmentation program for HSV channel. The robotic arm accurately regulates the machining position and duration. Dense ceramic oxide coatings of similar quality to the artificially prepared coatings were successfully prepared on aluminum (Al) surface. The surface morphology and elemental composition of the samples were characterized using scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and X-ray diffraction (XRD). The performance of the coatings was tested through thickness tests, hardness tests, and roughness tests. The tribological properties were tested by friction experiments, and its corrosion resistance was tested by electrochemical corrosion tests. The results confirm the effectiveness of a machine vision system for MAO process control, which is capable of preparing high-quality coatings comparable to manual operations. This not only solves the need for precise film formation in different application scenarios, but also provides a more efficient, controllable and innovative solution for the MAO process.

Thermal deflection in neutron scattering sample environments at Oak Ridge National Laboratory.

The neutron sources at Oak Ridge National Laboratory use a wide suite of sample environment equipment to deliver extreme conditions for a number of experiments. Much of this instrumentation focuses on extremes of temperature, such as cryostats, closed-cycle refrigerators in both low and high temperature configurations, and radiant heating furnaces. When the temperature is controlled across a large range, thermal deflection effects can notably move the sample and affect its alignment in the beam. Here, we combine these sample environments with neutron imaging and machine vision to determine the motion of a representative sample with respect to the neutron beam. We find vertical sample displacement on the order of 1-2mm and horizontal displacement that varies from near-negligible to 1.2mm. While these deflections are not relevant for some of the beamlines at the first target station at the spallation neutron source and the high flux isotope reactor, they will become critical for upcoming instrumentation at the second target station, as well as any instruments targeting sub-mm samples, as neutron sources and optics evolve to smaller and more focused beams. We discuss mitigation protocols and potential modifications to the environment to minimize the effect of misalignment due to thermal deflection.

Self-powered photodetector based on 1D TiO2-3D CdS mixed dimensional heterostructure fabricated at low temperature

Solution processed photodetectors have garnered great attention in applications such as, machine vision perception, neuromorphic computing and opto-electronic memory storage. Though, such photodetectors offer several advantages such as ease of fabrication, high scalability, low thermal budget and low-cost processing, multi-modal functionality etc. however, they suffer from the major drawback of inferior device performance −as low responsivity and slow rise time, particularly due to the intrinsic poor crystallinity of the photoactive material. In this work, we demonstrate a solution processed photodetector with impressive performance at comparatively low processing temperatures (<150 °C) based on the mixed dimensional heterostructure configuration of 1D TiO2 nanorods and 3D CdS nanoflowers. TiO2 nanorods have been synthesized by hydrothermal technique, whereas their CdS sensitization is done by chemical bath deposition. Low cost carbon paste is used as electrode instead of conventional non-economic noble metal electrodes. X-ray diffraction studies validated excellent crystallinity of the photoactive material even under low temperature processing condition. The type-II Heterojunction (TiO2 and CdS) configuration photodetector shows efficient response at zero bias, thus yielding a self-powered device. The detector shows response in UV and visible region, with excellent responsivity of 110 mA/W (5 V), 563 µA/W (0 V) and a quicker rise time of 81 ms. Albeit the simple fabrication scheme and low processing temperatures, the detector exhibited promising figures-of-merit, which aids in fabrication of novel solution processed photodetectors.

A machine vision approach with temporal fusion strategy for concrete vibration quality monitoring

In concrete construction, ensuring the quality of vibration is paramount for maintaining the strength, durability, and quality of structures. This study proposed a method for monitoring the vibration quality of vibrating robots to replace subjective human judgment. The method employed an improved EfficientNetV2 model to classify the vibration quality into four levels: unqualified, middle, qualified, and over-vibration. A temporal fusion strategy was introduced, employing time-domain probability fusion to enhance the accuracy and stability of the results. Additionally, image patching was applied to reduce computational complexity while preserving feature integrity. Experimental results demonstrate that the proposed method outperforms common mainstream models, achieving an accuracy of 96.47%, with a relatively small parameter size of 13.8 M. Compared to non-temporal fusion strategy, the accuracy is improved by 2.06%. This research has been successfully applied in practical engineering, providing a reliable means of quality assurance for concrete structures and demonstrating potential application prospects in the field of engineering construction.

Contagious infection-free medical interaction system with machine vision controlled by remote hand gesture during an operation

Contagious infection-free medical interaction system with machine vision controlled by remote hand gesture during an operation

Deep convolutional neural network for weld defect classification in radiographic images

The quality of welds is critical to the safety of structures in construction, so early detection of irregularities is crucial. Advances in machine vision inspection technologies, such as deep learning models, have improved the detection of weld defects. This paper presents a new CNN model based on ResNet50 to classify four types of weld defects in radiographic images: crack, pore, non-penetration, and no defect. Stratified cross-validation, data augmentation, and regularization were used to improve generalization and avoid over-fitting. The model was tested on three datasets, RIAWELC, GDXray, and a private dataset of low image quality, obtaining an accuracy of 98.75 %, 90.255 %, and 75.83 %, respectively. The model proposed in this paper achieves high accuracies on different datasets and constitutes a valuable tool to improve the efficiency and effectiveness of quality control processes in the welding industry. Moreover, experimental tests show that the proposed approach performs well on even low-resolution images.

An artificial visual neuron with multiplexed rate and time-to-first-spike coding.

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.

End-to-end optimization of single-shot monocular metasurface camera for RGBD imaging

RGBD cameras that capture color and depth information have a wide range of applications such as robotics, autonomous driving, and cons umer electronics. Traditional RGBD imaging usually requires multi-cameras or extra active illumination which inevitably leads to bulky or complex imaging systems, hindering the growing demand for compact integrated optical devices. Optical metasurface have emerged as a powerful substitute to the traditional diffractive optical element due to the superior dispersion manipulation and extremely compact size. However, it is still a great challenge to realize a single-shot monocular metasurface camera, due to the strong and unignorable wavelength-dependent aberrations. In this work, we demonstrate an end-to-end compact single-shot monocular metasurface camera for RGBD imaging. By utilizing end-to-end joint optimization framework to learn metasurface physical structure in conjunction with deep neural networks reconstruction algorithm, the multidimensional light field information RGBD of a scene in a single shot can be reconstructed within a depth range of 0.5m. Compared with traditional lens-based RGBD imaging, our proposed metasurface-based imaging achieves an improvement of about 2dB in chromatic imaging and a 4 times increase in depth estimation accuracy. Our proposed scheme could facilitate the further development of the intelligent computational meta-optics in diverse fields ranging from machine vision to biomedical imaging.

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional agriculture. This work applies these techniques to address the color classification of rapeseed, which is of great significance in the field of rapeseed growth diagnosis research. To bridge the gap between machine vision and hyperspectral technology, a framework is developed that includes seed color calibration, spectral feature extraction and fusion, and the recognition modeling of three seed colors using four machine learning methods. Three categories of rapeseed coat colors are calibrated based on visual perception and vector-square distance methods. A fast-weighted visibility graph method is employed to map the spectral reflectance sequences to complex networks, and five global network attributes are extracted to fuse the full-band reflectance as model input. The experimental results demonstrate that the classification recognition rate of the fused feature reaches 0.943 under the XGBoost model, confirming the effectiveness of the network features as a complement to the spectral reflectance. The high recognition accuracy and simple operation process of the framework support the further application of hyperspectral technology to analyze the quality of rapeseed.

AGRICULTURAL FIELD MANAGEMENT PROBLEMS: FROM CLASSIC AGRO-TECHNOLOGIES TO ARTIFICIAL INTELLIGENCE

The text is straightforward and precise. The agricultural sector has been significantly impacted by artificial intelligence (AI) technologies such as expert systems, natural language processing, speech recognition, and machine vision. This is due to factors such as the rising global population, increasing demand for food, changing weather conditions, and water availability. AI has not only increased the quantity of work in agriculture but also improved its quality. Researchers and scientists are now adopting new IoT technologies in smart farming to enable farmers to use AI technology for the advancement of seeds, crop protection, and fertilizers. This would enhance the financial viability of farmers and contribute to the general economic growth of the nation. AI is being used in three primary areas in agriculture: soil and crop monitoring, predictive analytics, and agricultural robots. Farmers are increasingly using sensors and soil sampling to collect data for farm management systems, which will be utilized for future studies and analysis. This paper enhances the area by doing a study of artificial intelligence applications in the agriculture industry. The text provides an introduction to AI, covering several AI techniques used in the agricultural sector, such as machine learning, the Internet of Things (IoT), expert systems, image processing, and computer vision.

A comprehensive research on measurement and evaluation of intelligent meter reading systems

Abstract Based on the machine vision and artificial intelligence technologies, the reading and acquisition of meter values have become the mainstream technical solutions for real-time monitoring of production data in the current industrial field. At the same time, the number of intelligent instruments and meters is increasing, and the quality varies. There is a lack of scientifically measurement methods and quality trust for intelligent meter reading algorithms. To promote their reliable and widespread application in various fields, it is necessary to conduct in-depth research on the assessment system of artificial intelligence in intelligent meter reading systems. This paper comprehensively studies various aspects involved in the emerging measurement field of artificial intelligence evaluation at the current stage, providing scientific measurement and evaluation references for the quality trust of artificial intelligence in the field of meters.&amp;#xD;

Modern techniques in detecting, identifying and classifying fruits according to the developed machine learning algorithm

Recent developments in machine vision have opened up a wide range of applications, and farming is no exception. Deep learning (DL) has a wide range of applications because of its capacity to extract robust features from photos. Shape, color, and feel of many fruit species make it difficult to discover and classify fruits. When examining the effects of artificial intelligence on fruit identification and classification, we noted that, up until 2018, the majority of approaches relied on traditional machine learning (ML) techniques, while just a few ways took use of DL techniques for recognizing fruits and categorization. In this post, we thoroughly covered the datasets that many academics utilized, the useful descriptors, the application of model, and the difficulties of utilizing DL to identify and classify fruits. Finally, we compiled the outcomes of various DL techniques used in earlier research to identify and categorize fruits. This work examines the use of models based on DL for fruit categorization and recognition in recent studies. In order to make it simpler for beginning agricultural researchers to comprehend the importance of ML in the agricultural domain, we have developed a DL model for apple categorization using the well-known dataset "Fruit 360" starting scratch.

Application of Machine Vision Based on the Second Law of Thermodynamics in High-Temperature Industrial Inspection

Application of Machine Vision Based on the Second Law of Thermodynamics in High-Temperature Industrial Inspection

Fine-Grained Detection Model Based on Attention Mechanism and Multi-Scale Feature Fusion for Cocoon Sorting

Sorting unreelable inferior cocoons during the reeling process is essential for obtaining high-quality silk products. At present, silk reeling enterprises mainly rely on manual sorting, which is inefficient and labor-intensive. Automated sorting based on machine vision and sorting robots is a promising alternative. However, the accuracy and computational complexity of object detection are challenges for the practical application of automatic sorting, especially for small stains of inferior cocoons in images of densely distributed cocoons. To deal with this problem, an efficient fine-grained object detection network based on attention mechanism and multi-scale feature fusion, called AMMF-Net, is proposed for inferior silkworm cocoon recognition. In this model, fine-grained object features are key considerations to improve the detection accuracy. To efficiently extract fine-grained features of silkworm cocoon images, we designed an efficient hybrid feature extraction network (HFE-Net) that combines depth-wise separable convolution and Transformer as the backbone. It captures local and global information to extract fine-grained features of inferior silkworm cocoon images, improving the representation ability of the network. An efficient multi-scale feature fusion module (EMFF) is proposed as the neck of the object detection structure. It improves the typical down-sampling method of multi-scale feature fusion to avoid the loss of key information and achieve better performance. Our method is trained and evaluated on a dataset collected from multiple inferior cocoons. Extensive experiments validated the effectiveness and generalization performance of the HFE-Net network and the EMFF module, and the proposed AMMF-Net achieved the best detection results compared to other popular deep neural networks.

Single-metalens-assisted polarization imaging and edge detection for target recognition

Simultaneous capture of various light information, including polarization and edge information of the objects, has consistently been a fundamental concern within the field of target recognition. However, these tasks are typically accompanied by bulky optical components and active illumination methods, which significantly restricts their use in compact and lightweight applications. Here, we demonstrate a metalens-assisted imaging system that can simultaneously achieve polarization imaging and optoelectronic edge detection in a single shot with low consumption. The dielectric metalens is designed to achieve polarization imaging by dispersing the input polarized light into two orthogonal components, resulting in optoelectronic isotropic edge detection of two-dimensional images after digital post-processing. Compared with the algorithmic methods using a convolution kernel, the proposed system has a much lower computational complexity. The work presented in this study demonstrates the potential applications in machine vision and paves the way for the development of compact target recognition and real-time image processing systems.

Optimization of Layout Indoor Space and Design Based on Machine Vision Algorithm

In the realm of indoor space design, achieving an optimal layout is crucial for enhancing functionality, efficiency, and user experience. Traditional approaches often rely on manual assessments or simplistic algorithms, lacking the precision and adaptability required for modern architectural challenges. This paper presents a novel framework for optimizing indoor space layout and design leveraging advanced machine vision algorithms. By harnessing the power of computer vision techniques, our methodology offers automated analysis and optimization of indoor environments, considering factors such as spatial utilization, traffic flow, and aesthetic appeal. Through a comprehensive exploration of machine learning and optimization techniques, we demonstrate significant improvements in space efficiency and usability, paving the way for intelligent design solutions in various architectural contexts. This research contributes to the intersection of computer vision and architectural design, offering a paradigm shift towards data-driven and adaptive approaches for optimizing indoor spaces.

Analyzing Players' Tactical Positions and Movement Trajectories in Soccer Matches Using Machine Vision Algorithms

Tactical positions and movement trajectories in soccer matches using machine vision algorithms involves leveraging computer vision techniques to track players and the ball throughout the game. By processing video feeds from multiple cameras, machine vision algorithms can identify players, recognize their positions on the field, and map out their movement trajectories over time. This analysis provides valuable insights into team formations, player positioning, and tactical strategies employed during matches. Coaches and analysts can use this information to assess team performance, identify patterns in gameplay, and make data-driven decisions to optimize tactics and training regimes. Machine vision algorithms enhance the understanding of soccer dynamics, facilitating more strategic and effective approaches to coaching and gameplay.. This paper presents a comprehensive analysis of player movement, tactical positions, and detection in soccer matches using machine learning algorithms. Leveraging a simulation environment built upon a Recurrent Neural Network (RNN) model trained on historical match data, we investigate the efficacy of the model in accurately predicting future player positions based on contextual features such as player trajectories and ball movement. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, providing valuable insights for coaches and analysts in understanding team strategies and adapting to match dynamics. Additionally, we evaluate player detection systems to assess their capabilities and limitations, laying the groundwork for future improvements in player tracking technologies. Our findings underscore the potential of machine learning in soccer analytics, offering actionable insights for enhancing performance analysis, strategic decision-making, and overall understanding of the game. Our results show a high prediction accuracy, with an average Mean Squared Error (MSE) of 0.05. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, achieving an overall prediction accuracy of 85%. Additionally, we evaluate player detection systems to assess their capabilities and limitations, achieving a detection accuracy of 90%.

Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots

Stones are one of the primary objects that impede the normal activity of underground pipelines. As human intervention is difficult inside a narrow underground pipe, a robot with a machine vision system is required. In order to remove the stones during regular robotic inspections, precise stone detection, segmentation, and measurement of their distance from the robot are needed. We applied Mask R-CNN to perform an instant segmentation of stones. The distance between the robot and the segmented stones was calculated using spatial information obtained from a lidar camera. Artificial light was used for both image acquisition and testing, as natural light is not available inside the underground pipe. ResNet101 was chosen as the foundation of the Mask R-CNN, and transfer learning was utilized to shorten the training time. The experimental results of our model showed that the average detection precision rate reached 92.0; the recall rate was 90.0%; and the F1 score rate reached 91.0%. The distance values were calculated efficiently with an error margin of 11.36 mm. Moreover, the Mask R-CNN-based stone detection model can detect asymmetrically shaped stones in complex background and lighting conditions.