Digital Image Processing Technology Research Articles

Experimental and statistical study on sound absorption coefficient of porous asphalt concrete considering mesoscopic pore parameters

Porous asphalt concrete (PAC) has attracted significant attention in the road industry due to its excellent noise reduction capabilities. However, clogging in PAC decreases its sound absorption performance, as mesoscopic pore parameters play a crucial role in influencing PAC’s sound absorption abilities. To further investigate the influence of mesoscopic pore parameters on the sound absorption performance of PAC, CT scanning and digital image processing technology were employed. The principal component analysis method was employed to determine the weighting of the influence of mesoscopic pore parameters on the sound absorption coefficient. The correlation between different pore parameters and the sound absorption coefficient of PAC specimens was analyzed. The results showed that the minimum Feret diameter has the most significant effect on the sound absorption performance of PAC, while the concavity and the length-short axis ratio have the least impact. The correlation between mesoscopic pore parameters and sound absorption coefficients was found to be inconsistent before and after clogging. Mesoscopic pore parameters that were positively correlated with sound absorption coefficients before clogging turned negatively correlated after clogging. Connected large pores and smooth regular pore structure enhance the sound absorption performance of PAC, but clogging disrupts this structure, resulting in a decrease in sound absorption performance.

Sustainable Solution on Desiccation Crack Mitigation with Recycled Glass Sand

Desiccation cracking is a frequent natural phenomenon that occurs in drying soil and has a significant negative impact on the mechanical and hydraulic properties of clay or geomaterials in various engineering applications. In this study, recycled glass sand (RGS) was used to reduce the plasticity of clay soil and mitigate desiccation cracks in clay soils. The effect of the RGS particle size and content was investigated using a desiccation crack observation test. Digital image processing technology was used to evaluate the crack rate, length, width, and area during the observation test. The results reveal that the cracking rate was inversely proportional to the RGS content and directly proportional to the RGS particle size. For instance, the cracking rate of clay soil treated with 25% RGS with a particle size of 0.15 mm was reduced to 0.17% compared with untreated soil. The strengths of the untreated and RGS-treated soils were evaluated through unconfined compression tests. The unconfined compressive strength of the RGS-treated clay soil decreased slightly with the addition of RGS. In general, the addition of RGS has great potential for mitigating desiccation cracks in clay soils.

An Investigation of the Behaviors and Characteristics of the Interfacial Bonding Capacity between Cement Emulsified Asphalt Composite Binder and Reclaimed Asphalt Pavement

The interfacial bonding capacity between cement emulsified asphalt composite binder (CEACB) and reclaimed asphalt pavement (RAP) plays a critical role in improving the pavement performance of cold recycled asphalt emulsion mixtures (CRAEMs). This study aims to investigate the formation and development of the interfacial bonding capacity between CEACB and RAP. First, the dynamic wettability and the spreading behaviors of CEACB on RAP surfaces were explored according to the surface free energy theory. Second, digital image processing (DIP) technology was employed to recognize interfacial failure patterns. Lastly, the influence of internal and external factors on the interfacial bonding capacity between CEACB and RAP during the curing process was analyzed via grey relational analysis (GRA). The results indicate that a moderate cement content with a mass ratio of asphalt to cement equivalent to 1.0 can significantly enhance the wettability of CEACB on RAP surfaces. By appropriately prolonging the curing time and controlling the curing temperature, it is possible to increase the bonding strength between CEACB and RAP. Additionally, a strong correlation exists between initial wettability and ultimate bonding capacity during the bonding strength curing process. The good wettability that developed in the initial stage of interfacial strength formation relates to the decreased spalling rate of CEACB on the RAP surface. This study is not only devoted to understanding the mechanisms that can enhance CRAEM performance but also provides important guidance for practical engineering applications of cold recycled asphalt pavements.

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.

YOLOv5s fabric defect detection model with mixed attention mechanism

In the field of industrial quality inspection, fabric defect detection is regarded as an important research topic. The traditional inspection method mainly relies on manual visual inspection, which is time-consuming and easy to be affected by human factors, and has the problems of low accuracy, slow speed and high cost. Therefore, it is very important to use digital image processing technology and target detection technology to develop high precision, high efficiency and low-cost fabric defect detection model suitable for application deployment. Driven by deep learning-based neural network technology, many models with excellent performance have emerged in the field of object detection. This paper will improve on the basis of YOLOv5s model. ACmix module is introduced in Backbone layer of YOLOv5s model. This module overcomes the problem that convolution operation may cause local information loss after feature extraction, improves the model's limitation on feature extraction of complex background images, and enhances the model's processing ability in local feature fusion. Compared with the original YOLOv5s algorithm, mAP has improved by 0.6%. In the complex background, the detection accuracy of fabric defects targets has been significantly optimized and improved.

Digital image processing: A new tool for morphological measurements of freshwater turtles under rehabilitation.

Freshwater fauna is facing an uphill task for survival in the Ganga Basin, India, due to a range of factors causing habitat degradation and fragmentation, necessitating conservation interventions. As part of the ongoing efforts to conserve the freshwater fauna of the Basin, we are working on rehabilitating rescued freshwater chelonians. We carry out various interventions to restore rescued individuals to an apparent state of fitness for their release in suitable natural habitats. Morphometric measurements are crucial to managing captive wild animals for assessing their growth and well-being. Measurements are made using manual methods like vernier caliper that are prone to observer error experience and require handling the specimens for extended periods. Digital imaging technology is rapidly progressing at a fast pace and with the advancement of technology. We acquired images of turtles using smartphones along with manual morphometric measurements using vernier calipers of the straight carapace length and straight carapace width. The images were subsequently processed using ImageJ, a freeware and compared with manual morphometric measurements. A significant decrease in the time spent in carrying out morphometric measurements was observed in our study. The difference in error in measurements was, however, not significant. A probable cause for this may have been the extensive experience of the personnel carrying out the measurements using vernier caliper. Digital image processing technology can cause a significant reduction in the stress of the animals exposed to handling during measurements, thereby improving their welfare. Additionally, this can be used in the field to carry out morphometric measurements of free-ranging individuals, where it is often difficult to capture individuals, and challenges are faced in obtaining permission to capture specimens.

Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8

Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.

Effect of natural fractures on mechanical properties and fracture patterns of shale at microscopic scale: an example from the Lower Cambrian Niutitang formation in Qianbei region

In order to explore the influence of natural fractures on the mechanical properties and failure modes of shale at the micro scale, uniaxial compression numerical experiments were conducted on the shale of the Niutang Formation in northern Guizhou with different natural fracture angles using a rock failure process system and digital image processing technology. It is shown that the compressive strength of shale increases with the increase of natural crack inclination, and the growth rate of shale compressive strength also increases. Shale's microscopic fractures can generally be classified into four categories. The first category is to sprout along the natural cracks to the outside of the shale, and eventually form a crack similar to the "X" type (0°); the second category is to sprout along the natural cracks to the middle and outside of the shale, and eventually form an inverted "Y" type crack (15°, 30°); the third category is to sprout along the natural cracks to the middle and outside of the shale, and eventually form an inverted "Y" type crack (15°, 30°); the second type sprouts along the natural fractures toward the middle and outside of the shale, forming inverted "Y"-type fractures (15°, 30°); the third type cracks along the sides of the natural fractures, forming "Y"-type fractures (45°); and the fourth type does not crack along the natural fractures, forming "S"-type fractures (60°, 75°, and 90°). In the low natural fracture dip shale model, tensile damage mainly occurs, accompanied by a small amount of compressive shear damage; in the high natural fracture dip shale model, tensile damage and compressive shear damage account for a larger proportion in the fracture process.This suggests that the presence of natural cracks in shale has a significant impact on stress distribution. There are two main types of acoustic emission signal distribution and evolutionary features, the evolutionary features of acoustic emission signal distribution are of two types, 0°-45° test and 60°-90° test, and the difference is mainly reflected in the damage stage, the damage of shale with high natural fracture inclination is more intense, which is manifested by the decrease in the number of acoustic emission events, but the level of acoustic emission events in the damage stage is higher, which can reach 61788, 46605 and 94315, the shale with high natural fracture inclination is more brittle.

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

Plant diseases pose a serious threat to a country's economy and food security. One way to identify diseases in plants is through the visible features on their leaves. Farmers need to conduct an active examination of the condition of the leaves of plants to eradicate this disease. In this case, automatic recognition and classification of diseases of leaf crops is required in order to obtain an accurate identification. Digital image processing technology can be used to solve this problem. One effective approach is the Convolutional Neural Network (CNN). The trial image used a dataset consisting of 4000 images of mango leaf disease, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould. This study aims to compare the accuracy of CNN, VGG16 and InceptionV3. Architectural modeling uses these drawings to train and test models in recognizing and classifying mango leaf diseases. The results of modeling trials in the three scenarios were most optimally obtained by VGG16 with an accuracy of 96.87%, then InceptionV3 with an acquisition of 96.50% and CNN by 81%.

Research on the mixing characteristics of the bottom blowing molten pool based on flow characteristics and mixing uniformity

In this study, a new method of combining lance–liquid flow characteristics and mixing uniformity is proposed to evaluate the stirring characteristics in the bottom blowing copper molten pool. A fluid simulation model of a bottom blowing molten pool was established, water was used to simulate the melt environment, and an experimental platform was set up for verification. The effects of swirl, multi-channel, and straight pipe spray on the lance–liquid stirring characteristics of the bottom-blown copper molten pool are compared through quantifying the flow characteristics and mixing uniformity. In addition, digital image processing technologies, such as image entropy variance and eddy current map entropy increase, are introduced. Through numerical simulation research, it is found that the transverse velocity of the swirl spray lance is the largest, which makes the rise time of the bubble increase to the greatest extent. Compared with the straight pipe spray, the swirl spray reduces the liquid splash height by 0.054 m, and the degree of vortex flow is higher. The lance phase stability is increased by 37.87%, and the maximum turbulent kinetic energy can be increased by 8.73%. The spray effect of the multi-channel spray is between the two. It is shown that the swirling spray lance can improve the stability of gas in the molten pool, enhance the uniformity of gas–liquid mixing, and improve the operation cycle and the smelting efficiency of the molten pool.

A new numerical method for constructing the three-dimensional microstructure of S-RM using digital image processing technology

A new numerical method for constructing the three-dimensional microstructure of S-RM using digital image processing technology

Investigation into the effect of the blend ratio on the structure and properties of dual-feed-opening rotor-spun yarn

The scope of this study was to investigate the effect of the blend ratio on the structure and properties of dual-feed-opening rotor-spun yarn. A variety of yarns with different blended ratios were spun from two viscose fibers with the same properties but different colors. Through digital image processing technology, the ratios of the area occupied by the two fibers on the surface and the cross-section of the yarns were obtained. The results showed that the yarns with blend ratios of 40/60 and 50/50 had an obvious spacing phenomenon of two fiber segments on the surface. The ratio of the area occupied by the two fibers on the yarn surface was not equal to the value of the blend ratio. In the yarn cross-section, the difference in the area ratio of the two fibers to the blend ratio was subtle. The blend ratio had little effect on the mechanical properties (strength and elongation at break). However, the quantities of hairiness fluctuated greatly. The mass irregularity and imperfections decreased as the blend ratio approached 50/50. This work provides important guiding insights for the research of dual-feed-opening rotor-blended yarn.

Design of 3D Environment Combining Digital Image Processing Technology and Convolutional Neural Network

As virtual reality technology advances, 3D environment design and modeling have garnered increasing attention. Applications in networked virtual environments span urban planning, industrial design, and manufacturing, among other fields. However, existing 3D modeling methods exhibit high reconstruction error precision, limiting their practicality in many domains, particularly environmental design. To enhance 3D reconstruction accuracy, this study proposes a digital image processing technology that combines binocular camera calibration, stereo correction, and a convolutional neural network (CNN) algorithm for optimization and improvement. By employing the refined stereo-matching algorithm, a 3D reconstruction model was developed to augment 3D environment design and reconstruction accuracy while optimizing the 3D reconstruction effect. An experiment using the ShapeNet dataset demonstrated that the evaluation indices—Chamfer distance (CD), Earth mover’s distance (EMD), and intersection over union—of the model constructed in this study outperformed those of alternative methods. After incorporating the CNN module in the ablation experiment, CD and EMD increased by an average of 0.1 and 0.06, respectively. This validates that the proposed CNN module effectively enhances point cloud reconstruction accuracy. Upon adding the CNN module, the CD index and EMD index in the dataset increased by an average of 0.34 and 0.54, respectively. These results indicate that the proposed CNN module exhibits strong predictive capabilities for point cloud coordinates. Furthermore, the model demonstrates good generalization performance.

Simulation of Fracture Process of Lightweight Aggregate Concrete Based on Digital Image Processing Technology

Simulation of Fracture Process of Lightweight Aggregate Concrete Based on Digital Image Processing Technology

Intelligent analysis of corrosion characteristics of steel pipe piles of offshore construction wharfs based on computer vision

The lower part of offshore construction wharfs is mostly a steel structure system composed of steel pipe piles, whose corrosion level directly affects the structural safety performance of steel wharfs in service. The currently common corrosion detection methods can only sample and inspect steel pile after it has been dismantled, making it impractical for in-service monitoring during the operational period of the steel pile. In this paper, a deep learning-based image classification model is first established to recognize the type of corroded area on steel pipe piles. The model achieves a recognition accuracy of 99.14 % in automatically identifying different types of corroded areas, including full immersion zone, tidal range zone, and splash zone. Subsequently, digital image processing technology is utilized to automatically calculate the corroded area of steel pipe piles. The method proposed in this paper can obtain the key information, such as type of corrosion area and area of the steel pipe pile corrosion area, without damaging their structural performance during the service. With this data, the mechanical performance of steel pipe piles can be analyzed, and the structural safety of the in-service steel pipe piles can be determined, thereby ensuring the safety of the construction wharf.

Radiation Dose and Image Quality with Exposure Factor Variation Using a Virtual Grid in Digital Radiography

Digital radiography technology provides many advantages. However, there are still frequent repetitions of inspections due to failure to determine the exposure factor due to a decrease in image quality. Virtual Grid is a digital radiographic image processing technology that converts image quality that is deteriorating due to X-ray scattering to better image quality by reducing the effects of X-ray scattering. Application of a virtual grid can contribute to improving image quality and increasing the procedural efficiency of the workflow in a radiographic examination. This study uses a research-experimental design, with a One-Shot Case Study. The sample selection of 60 samples was carried out randomly by judgmental or purposive sampling. The sampling technique was carried out with specific considerations for the research objectives to determine the optimal exposure factor by using a virtual grid for the skull, lumbar, and pelvic radiographic examinations. Then, it was analyzed quantitatively and qualitatively visually by three radiologists—a bivariate analysis of data using one-way ANOVA. Qualitative analysis was carried out as well as a test. Feel free to assess the agreement of the informants. Results In the quantitative and qualitative analysis, the exposure factor and the ideal virtual grid ratio for optimization are skull AP: 106 kV, 2 mAs, ratio 14:1, skull lateral: 106 kV, 1.25 mAs, ratio 14:1, skull lumbar AP: 106 kV, 4 mAs, ratio 14:1, skull lumbar lateral: 113 kV, 6.3 mAs, 10:1 ratio, and pelvis AP: 92 kV, 8 mAs, 14:1 ratio.

Detection of cracks in aircraft structures using digital image processing technology

Full-scale aircraft structural fatigue testing exposes structural weaknesses by simulating the use of the aircraft in service and monitoring the development and expansion of fatigue cracks to guide the optimisation of aircraft structural design, the determination of service life and the development of maintenance and repair programmes. If crack damage is not detected in time, the uncontrolled expansion of the crack may lead to structural damage and prolong the test cycle. Therefore, it is crucial to achieve accurate detection of early cracks in fatigue testing of aircraft structures. Digital image technology is a new technology supported by a number of technical and theoretical achievements. In practical application, it relies on the support of computer functions, by converting image information into two-dimensional digital signals, thus realising various operations. In this paper, digital image processing techniques are used to deepen the backbone feature extraction network structure and improve the prediction scale of small targets, thus improving the performance of the model in terms of feature extraction and small defect detection. Experimental results show that the method can achieve accurate detection of small cracks, providing an effective technical approach for the automatic detection of cracks in aircraft structural fatigue tests. The technique of detecting cracks in aircraft structures using digital image processing also provides a new idea for research into improving aircraft endurance testing, which can be further developed with the aid of image technology.

Experimental study of hydraulic properties of the granite fracture under heating-cooling cycles

The fracture within the surrounding rock will suffer heating-cooling process for the high-level radioactive waste (HLW) geological repository after the disposal of the HLW, and the conductivity variation of fracture to the heating-cooling process is crucial for the long-term safety of the HLW repository. To study the response of hydraulic properties to temperature of granite fracture, the hydraulic conductivity measurement of granite fracture was carried out during heating-cooling cycles (25 °C-120 °C-25 °C) with different confining pressures (3 MPa, 5 MPa and 8 MPa). In addition, the variation of morphology, pore structure and mineral distribution of fractured granite were analyzed using nuclear magnetic resonance and digital image processing technology. Results show that the hydraulic aperture continuously decreased with increasing temperature, then slightly recovered with the decrease of temperature during the first heating-cooling process. For the second heating-cooling process, the evolution of the hydraulic aperture with temperature is similar to the first heating-cooling cycle, while the variation magnitude was decreased significantly. The confining pressure affect the recovery ability of hydraulic aperture during the cooling process, the hydraulic aperture recover decreased with the increasing confining pressure. Combined effects of the confining pressure and thermal effect control the plastic deformation and fracture aperture of granite fracture. A formula considering the influence of temperature and confining pressure was developed to characterize the hydraulic aperture of granite fracture. In addition, variations in morphyology and mineral distribution were analyzed of granite fracture before and after the test. Result shows that the roughness of fracture surfaces decreased, and biotite and feldspar increased while quartz decreased after tests. Findings from the study could provide important reference for the HLW geological disposal engineering.

Mesoscopic Damage and Fracture Characteristics of Hard Rock under High-Frequency Ultrasonic Vibration Excitation

Ultrasonic high-frequency vibrational fracture technology can compensate for the deficiencies of traditional fracture methods and has promising applications in underground rock drilling engineering. In this study, ultrasonic high-frequency vibrational tests were performed on brittle fine-grained red sandstone in combination with CT real-time scanning, which revealed mesoscopic fracture processes in the rock. Digital image processing technology is used to identify and extract the pores of CT images, and the pore evolution law of rock slices at different layers under ultrasonic vibration excitation is quantitatively studied. The results show that the increase in porosity decreases with increasing distance from the excitation surface, with the lowest layers of the rock showing an increase in porosity of only 0.22%. In addition, a mechanical model of rock breaking by ultrasonic vibrations was derived to explain the non-uniform damage mechanism of rock space under ultrasonic vibration excitation.

Detection Method of Cracks in Expressway Asphalt Pavement Based on Digital Image Processing Technology

Considering the limitations of the current pavement crack damage detection methods, this study proposes a method based on digital image processing technology for detecting highway asphalt pavement crack damage. Firstly, a non-subsampled contourlet transform is used to enhance the image of highway asphalt pavement. Secondly, the non-crack regions in the image are screened, and the crack extraction is completed by obtaining and enhancing the crack intensity map. Finally, the features of cracks are extracted and input into the support vector machine for classification and recognition to complete the detection of cracks in highway asphalt pavement. The experimental results show that the proposed method can effectively enhance the quality of a pavement image and precisely extract a crack area from the image with a high level of damage detection accuracy.