Advanced Image Processing Technology Research Articles

Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production.

Ensemble Deep Learning Models for Heart Failure Prediction from Real Healthcare Records

Cardiovascular diseases (CVDs) remain the leading cause of death globally, necessitating advancements in non-invasive and efficient monitoring techniques. "Retinabeat" proposes a novel approach to CVD monitoring by leveraging the diagnostic potential of retinal vascular patterns. Utilizing state-of-the-art retinal imaging combined with the VGG19 convolutional neural network, this system aims to detect and analyze subtle changes in the retinal vasculature that correlate with cardiovascular health. Preliminary studies suggest that these retinal indicators can serve as reliable markers of cardiovascular anomalies, providing a non-invasive, cost-effective, and readily accessible method for early detection and ongoing management of heart health. This paper details the development and validation of the Retinabeat system, evaluates its performance against traditional methods, and discusses the implications of integrating advanced image processing technologies in routine cardiovascular care. The outcomes highlight the system’s potential to enhance predictive diagnostics and personalize health monitoring, paving the way for broader applications in telemedicine and preventive healthcare. Keywords— Cardiovascular diseases, VGG19, telemedicine, Retinal images

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection

Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking.

Complete autonomous systems such as self-driving cars to ensure the high reliability and safety of humans need the most efficient combination of four-dimensional (4D) detection, exact localization, and artificial intelligent (AI) networking to establish a fully automated smart transportation system. At present, multiple integrated sensors such as light detection and ranging (LiDAR), radio detection and ranging (RADAR), and car cameras are frequently used for object detection and localization in the conventional autonomous transportation system. Moreover, the global positioning system (GPS) is used for the positioning of autonomous vehicles (AV). These individual systems' detection, localization, and positioning efficiency are insufficient for AV systems. In addition, they do not have any reliable networking system for self-driving cars carrying us and goods on the road. Although the sensor fusion technology of car sensors came up with good efficiency for detection and location, the proposed convolutional neural networking approach will assist to achieve a higher accuracy of 4D detection, precise localization, and real-time positioning. Moreover, this work will establish a strong AI network for AV far monitoring and data transmission systems. The proposed networking system efficiency remains the same on under-sky highways as well in various tunnel roads where GPS does not work properly. For the first time, modified traffic surveillance cameras have been exploited in this conceptual paper as an external image source for AV and anchor sensing nodes to complete AI networking transportation systems. This work approaches a model that solves AVs' fundamental detection, localization, positioning, and networking challenges with advanced image processing, sensor fusion, feathers matching, and AI networking technology. This paper also provides an experienced AI driver concept for a smart transportation system with deep learning technology.

Robotic vs. laparoscopic resection for hepatocellular carcinoma

Minimally invasive liver surgery (MILS) has become increasingly popular over the last two decades, with hepatocellular carcinoma (HCC) representing a common indication. While data has shown the benefits of a laparoscopic vs. an open approach, robotic liver surgery is rapidly emerging. In this context, among the two minimal access approaches [robotic (RLR) vs. laparoscopic liver resection (LLR)], a differential benefit is still under investigation. While the advantages of RLR include increased dexterity, reduction of physiological tremors, and wrist articulation, it currently has no haptic feedback or specialized liver parenchymal transection devices and is associated with increased operative time and cost. However, RLR has proved to be a safe and effective approach for select patients with HCC. Some benefits of RLR include similar oncological outcomes to open or laparoscopic surgery, possibly reduced conversion rates, and an easier transition from open surgery to a minimally invasive approach. Moreover, while already today RLR can facilitate resection for HCC in hard-to-reach anatomic locations (e.g., transthoracic approach to posterior-superior liver), the future of robotics with the development of advanced image processing technologies, haptic feedback, liver-specific devices, lower cost, and more robot choices seems even more promising.

Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

AbstractQuantifying the microstructure of materials is of significance in material development, especially for building the relationship between structure and property. To establish a remarkable structure‐property (SP) linkage, a novel concept referred to as irregular‐representative volume element (IRVE) based on panoramic image stitching technology (PIST) is proposed and a data‐driven scheme integrating irregular domain‐oriented two‐point statistics, principal component analysis (PCA), and symbolic regression based on genetic programming (GPSR) is constructed. Combining with advanced image processing and genetic programming technologies, this scheme improves the microstructure quantization framework. This scheme can not only be applied in different complex conditions for extracting the information of a material microstructure, but can also to embody details of microstructure from the perspective of large scale. IRVE is demonstrated to have both strong statistical representativeness and sufficient physical interpretation, which makes the scheme robust and reliable. Performing the scheme on an example of ferrite heat‐resistant steels, it shows a powerful ability in building an equational SP linkage with high precision (R = 0.91, RMSE = 13.17), the generalization ability of the linkage is also validated by an unseen steel (relative percentage error is 2.66%). The scheme has bright application prospects in predicting mechanical property and accelerating alloy design.

DEEP LEARNING FOR SEMANTIC SEGMENTATION OF CORAL IMAGES IN UNDERWATER PHOTOGRAMMETRY

Abstract. Regular monitoring activities are important for assessing the influence of unfavourable factors on corals and tracking subsequent recovery or decline. Deep learning-based underwater photogrammetry provides a comprehensive solution for automatic large-scale and precise monitoring. It can quickly acquire a large range of underwater coral reef images, and extract information from these coral images through advanced image processing technology and deep learning methods. This procedure has three major components: (a) Generation of 3D models, (b) understanding of relevant corals in the images, and (c) tracking of those models over time and spatial change analysis. This paper focusses on issue (b), it applies five state-of-the-art neural networks to the semantic segmentation of coral images, compares their performance, and proposes a new coral semantic segmentation method. Finally, in order to quantitatively evaluate the performance of neural networks for semantic segmentation in these experiments, this paper uses mean class-wise Intersection over Union (mIoU), the most commonly used accuracy measure in semantic segmentation, as the standard metric. Meanwhile, considering that the coral boundary is very irregular and the evaluation index of IoU is not accurate enough, a new segmentation evaluation index based on boundary quality, Boundary IoU, is also used to evaluate the segmentation effect. The proposed trained network can accurately distinguish living from dead corals, which could reflect the health of the corals in the area of interest. The classification results show that we achieve state-of-the-art performance compared to other methods tested on the dataset provided in this paper on underwater coral images.

Urban Traffic Monitoring and Analysis Using Unmanned Aerial Vehicles (UAVs): A Systematic Literature Review

Unmanned aerial vehicles (UAVs) are gaining considerable interest in transportation engineering in order to monitor and analyze traffic. This systematic review surveys the scientific contributions in the application of UAVs for civil engineering, especially those related to traffic monitoring. Following the PRISMA framework, 34 papers were identified in five scientific databases. First, this paper introduces previous works in this field. In addition, the selected papers were analyzed, and some conclusions were drawn to complement the findings. It can be stated that this is still a field in its infancy and that progress in advanced image processing techniques and technologies used in the construction of UAVs will lead to an explosion in the number of applications, which will result in increased benefits for society, reducing unpleasant situations, such as congestion and collisions in major urban centers of the world.

Potential of a new water-soluble agent for enhancing heavy oil recovery: A pore-scale investigation

The two-phase viscosity modifier (T-PVM) is a new chemical agent for enhancing sweep and displacement efficiency in heavy oil reservoirs due to multiple-effects of reducing oil viscosity by emulsifying and increasing water viscosity. In the work, the dynamic interfacial tension (IFT) between simulated oil and chemical solution, viscosity, rheological characteristics, emulsification and stability of emulsion are measured to evaluate performance difference among the agents. Then, microfluidic visualization experiments were performed for subsequent chemical flooding after initial water flooding. Based on the advanced image processing technology, morphology and migration characteristics of remaining oil were described quantitatively and the mechanism of enhanced oil recovery was explored. Results show that large amount of residual oil are remaining in the non-main streamline area and dead-end pores after water flooding, which is observed in the morphology of spot, membrane, column, cluster and network. There would be migration path separation between polymer and surfactant during surfactant-polymer (SP) combination flooding. However, T-PVM, as a single chemical agent, penetrates in remaining oil and emulsifies columnar and membrane-shaped oil, which erodes the cluster and network-shaped oil simultaneously. Compared to the traditional viscosity reducer (VR) and SP flooding, multi-effective T-PVM enhances microcosmic sweep efficiency by approximately 35.93% and 21.83%, respectively. It is thereby expected that T-PVM flooding is effective for enhancing heavy oil recovery using the chemical cold flooding.

Deep Learning Based Image Forgery Detection Methods

Increasingly advanced image processing technology has made digital image editing easier and easier. With image processing software at one’s fingertips, one can easily alter the content of an image, and the altered image is so realistic that it is illegible to the naked eye. These tampered images have posed a serious threat to personal privacy, social order, and national security. Therefore, detecting and locating tampered areas in images has important practical significance, and has become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely used in image tampering localization, and the achieved performance has significantly surpassed traditional tampering forensics methods. This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning. According to the two types of tampering detection based on deep learning, the detection tasks of the method are detailed separately, and the problems and future prospects in this field are discussed. It is quite different from the existing work: 1) This paper mainly focuses on the problem of image tampering detection, so it does not elaborate on various forensic methods. 2) This paper focuses on the detection method of image tampering based on deep learning. 3) This paper is driven by the needs of tampering targets, so it pays more attention to sorting out methods for different tampering detection tasks.

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management of dangerous situations, with the help of systems and modules designed in this direction. The approach and classification of situations that can cause accidents is another feature analyzed in this paper, including detecting elements of a psychosomatic nature: analysis and detection of the conditions a driver goes through, pedestrian analysis, and maintaining a preventive approach, all of which are embedded in a modular architecture. The versatility and usefulness of such a system come through its ability to adapt to context and the ability to communicate with traffic safety systems such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-everything), and VLC (visible light communication). All these elements are found in the operation of the system and its ability to become a portable device dedicated to road safety based on (radio frequency) RF-VLC (visible light communication).

Liquid Seepage in Coal Granular-Type Porous Medium.

To investigate liquid seepage process in a coal granular-type porous medium, a new sampling device was designed to obtain coal samples with required porosity. Meanwhile, an approach combining ultra-deep-field microscopy with advanced digital image processing technologies was proposed to rebuild granular-type porous medium models. The liquid seepage process was simulated with CFD, and the effects of head pressure, liquid viscosity, and pore size were studied. The results show that only liquids with head pressures above a critical value can penetrate into coal stacks and the hydraulic conductivity and permeability are positively correlated to the driving head pressure. Liquid viscosity enhances flow deformation, causing more eddy current energy dissipation; the turbulent eddy dissipation caused by acetone, methanol, and ethanol was 700, 1200, and 4700 m2/s3, respectively. Larger pores can strengthen the additional pressure at the front end of the flow, reducing the flow resistance and thus increasing the fluid kinetic energy and seepage velocity.

Automatic Human Dendritic Cells Segmentation Using K-Means Clustering and Chan-Vese Active Contour Model

Background and objectiveNowadays, the number of pathologies related to food are multiplied. Mycotoxins are one of the most severe food contaminants that cause serious effects on the human health. Therefore, it is necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, a new investigational method using human dendritic cells was endorsed by biologists. Nevertheless, analysis of the morphological features and the behavior of these cells remains merely visual. In addition, this manual analysis is difficult and time-consuming. Here, we focus mainly on automating the evaluation process by using advanced image processing technology. MethodsAn automatic segmentation approach of microscopic dendritic cell images is developed to provide a fast and objective evaluation. First, a combination of K-means clustering and mathematical morphology is used to detect dendritic cells. Second, a region-based Chan-Vese active contour model is used to segment the detected cells more precisely. Finally, dendritic cells are extracted by a filtering based on eccentricity measure. ResultsThe proposed scheme is tested on an actual dataset containing 421 microscopic dendritic cell images. The experimental results show high conformity between the results of the proposed scheme and ground-truth elaborated by biological expert. Moreover, a comparative study with other state-of-art segmentation schemes demonstrates the efficiency of the proposed method. It gives the highest average accuracy rate (99.42 %) compared to recent studied approaches. ConclusionsThe proposed image segmentation method for morphological analysis of dendrite inhibition can consistently be used as an assessment tool for biologists to facilitate the evaluation of serious health impacts of mycotoxins.

Description of Gas Hydrate Using Digital Core Technology

Abstract As a new energy source with abundant resources, clean combustion, and high calorific value, gas hydrates have received much attention in recent years. However, the sampling cost is relatively high because the gas hydrates exist in deep seas and frozen soils. Digital core technology can reconstruct hydrate cores without destroying rock samples. In this paper, the advanced image processing technology is used to process the gas hydrate computed tomography (CT) scan image, and a three-dimensional hydrate digital core model is constructed, which can depict the sample's pore structure features. avizo software is used for filtering and image segmentation; the porosity is calculated as 35.90%, the hydrate saturation is 36.92%, and the pore network model is established. The pore radius is mostly distributed in 0–1 × 102 µm, and the average pore radius is 168.131 µm; the throat radius is mostly distributed in 0.5–1×102 µm. The seepage simulation on the pore scale is carried out, and the absolute permeability is calculated to be 76.8 µm2. Compared with conventional physical experiments, the digital core technology can obtain the true distribution of the pores inside the hydrate core, which is very helpful for analyzing the physical parameters of the hydrate core. The digital core technology is of great significance in the study of hydrate reservoirs.

StoolNet for Color Classification of Stool Medical Images

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.

Improvement of Methods for Predicting the Generation Capacity of Solar Power Plants: the Case of the Power Systems in the Republic of Crimea and City of Sevastopol

The construction and operation of large solar power plants (SPPs) and the dependence of their production on light and other meteorological factors leads to a strong dependence of the operation modes of the Republic of Crimea and Sevastopol power system on meteorological factors. Today, given that the share of solar power plants is about 30% of the total installed capacity, it is necessary to solve the problems that have a great impact on the power system operating modes. With large output capacity of the solar power plant, the operator has to give commands to turn off the generating equipment of thermal power plants. In power systems with a large share of solar generation, it is necessary to solve this problem by improving the generated power predicting methods, as it will reduce the dependence of operating modes on weather factors and increase the reliability of the power system. The paper discusses the use of hybrid predicting methods that imply taking into account the possibility of the weather scenarios simulation, advanced cloud-based image processing technology, and close-to-real-time cloud motion surveillance cameras. There was an experimental software created that selects coefficients of set configuration time series. In combination with the conservative methods, it makes predicting the SPP Perovo output more accurate. Taken together, the chosen methods of predicting solar power generation capacity in the power system of the Republic of Crimea and Sevastopol ensure not only stability of the power system as a whole, but also the maximum efficiency of power plants, allow to accelerate the integration of solar power plants into the power system, and have positive effects on the environment.

Effect of reinforced particle volume fraction on the failure behavior of TiC/AZ91 composites

The two-dimensional finite element model based onmicrostructure combined advanced image processing technology with the finite element model can truly reflect th e plane particle morphology and distribution of composite materials. In this paper, with the help of image processing and recognition technology, a finite element model of TiC/AZ91 magnesium matrix composite was established, and the longitudinal tensile test of magnesium matrix composite was simulated by finite element method to analyze the influence of enhanced particle volume fraction on the failure rule of its microstructure. The results showed that the TiC/AZ91 magnesium matrix composites were more like ly to fail as the volume fraction increased.

Using Asymmetric Theory to Identify Heterogeneous Drivers’ Behavior Characteristics Through Traffic Oscillation

This paper applies the asymmetric driving theory to capture driving characteristics of car-following behavior throughout traffic oscillation. Unmanned aerial vehicle (UAV) was employed to record videos near a bottleneck on an expressway in Nanjing, China, from which authors used advanced image processing technology to track and extract the high-fidelity vehicles trajectory data for a microscopic analysis in this study. First, with analyzing the individual vehicle trajectory throughout oscillation, authors find that the driving characteristics of drivers are heterogeneous but show several consistent features: before and after experiencing oscillation, relatively more drivers tend to maintain aggressive driving, i.e., 49.7% are originally aggressive (OA) and 37.2% are later aggressive (LA). Second, the statistical analysis indicates some representative characteristics of Chinese drivers: the polarization (aggressive or timid) is more significant before oscillation (OA 49.7%, originally Newell ON 18.9% and originally timid OT 31.4%) but changing to relative equilibrium after oscillation (LA 37.2%, later Newell LN 33.8% and later timid LT 29%). Finally, the authors also find that the type and intensity of each driver's reaction in oscillation are related to the characteristics he or she have before encountering oscillation. These findings of asymmetric driving behavior evolution in this paper may help to explain the causes of hysteresis and unstable traffic flow phenomena.

Effects of microscopic pore structure heterogeneity on the distribution and morphology of remaining oil

Abstract Waterflooding experiments were performed using Micro-CT on four cores of different pore structures from Donghe sandstone reservoirs in the Tarim Basin. The water, oil and grains were accurately separated by the advanced image processing technology, the pore network model was established, and parameters such as the number of throats and the throat size distribution were calculated to characterize the microscopic heterogeneity of pore structure, the flow of oil phase during displacement, and the morphology and distribution of remaining oil after displacement. The cores with the same macroscopic porosity-permeability have great differences in microscopic heterogeneity of pore structure. Both macro porosity-permeability and micro heterogeneity of pore structure have an influence on the migration of oil phase and the morphology and distribution of remaining oil. When the heterogeneity is strong, the water phase will preferentially flow through the dominant paths and the remaining oil clusters will be formed in the small pores. The more the number of oil clusters (droplets) formed during displacement process, the smaller the average volume of cluster is, and the remaining oil is dominated by the cluster continuous phase with high saturation. The weaker the heterogeneity, the higher the pore sweep efficiency is, and the remaining oil clusters are mainly trapped in the form of non-continuous phase. The distribution and morphology of micro remaining oil are related to the absolute permeability, capillary number and micro-heterogeneity. So, the identification plate of microscopic residual oil continuity distribution established on this basis can describe the relationship between these three factors and distribution of remaining oil and identify the continuity of the remaining oil distribution accurately.