Image Recognition Method Research Articles

A Machine Vision-Based Fiber Profile Image Recognition Method for Alignment of FBG Inscribing

A Machine Vision-Based Fiber Profile Image Recognition Method for Alignment of FBG Inscribing

Lifetime prediction of epoxy coating using convolutional neural networks and post processing image recognition methods

The rapid failure of organic coatings in deep-sea environments complicates accurate lifetime prediction. Given the rapid cracking characteristic on the coating surface in this environment, a comprehensive “performance-structure” failure model was established. Initially, a targeted image recognition approach containing convolutional neural network (CNN) and post-processing was constructed for the crack area detection. An overall precision of 82.81% demonstrated the network’s good accuracy. The length distribution and the statistical evolution of cracks were extracted from SEM images to obtain the kinetic equation of the cracks related to coating structure degradation. In addition, the kinetics of water diffusion and coating adhesion were examined, as they represent critical parameters of coating performance. Based on this achievement, a failure model incorporating three dominant factors was integrated by the gray relational analysis method. The average prediction error of the model was 2.60%, which lays the groundwork for developing image-based methods to predict coating life.

Research on tomato disease image recognition method based on DeiT

Tomatoes, globally cultivated and economically significant, play an essential role in both commerce and diet. However, the frequent occurrence of diseases severely affects both yield and quality, posing substantial challenges to agricultural production worldwide. In China, where tomato cultivation is carried out on a large scale, disease prevention and identification are increasingly critical for enhancing yield, ensuring food safety, and advancing sustainable agricultural practices. As agricultural production scales and the demand for efficient methodologies grows, traditional disease recognition methods no longer meet current needs. The agricultural sector's move towards more modern and scalable production methods necessitates more effective and precise disease recognition technologies to support swift decision-making and timely preventive actions. To address these challenges, this paper proposes a novel tomato disease recognition method that integrates the data-efficient image transformers (DeiT) model with strategies like exponential moving average (EMA) and self-distillation, named EMA-DeiT. By leveraging deep learning technologies, this method significantly improves the accuracy of disease recognition. The enhanced EMA-DeiT model demonstrated exemplary performance, achieving a 99.6 % accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2 % on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1 % accuracy on the PlantDoc dataset and 97.6 % on the Tomato-Village dataset. Utilizing the improved DeiT model, a comprehensive tomato disease recognition system was developed, featuring modules for image collection, disease detection, and information display. This system facilitates an integrated process from image collection to intelligent disease analysis, enabling agricultural workers to promptly understand and respond to disease occurrences. This system holds significant practical value for implementing precision agriculture and enhancing the efficiency of agricultural production.

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Abstract To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building's refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

DDFA: a displacement and diffusion-based feature augmentation method for imbalanced image recognition

DDFA: a displacement and diffusion-based feature augmentation method for imbalanced image recognition

Bearing fault diagnosis based on novel hierarchical multiscale dispersion entropy in corresponding color block images

Abstract The rolling bearing is a critical component of mechanical equipment, and its failure can lead to serious consequences. In order to effectively extract fault features of rolling bearings and improve fault diagnosis performance, a fault diagnosis framework based on hierarchical multiscale dispersion entropy (HMDE) and improved histogram of oriented gradient (HOG) is proposed by combining entropy method with image recognition method. Firstly, the original vibration signal is subjected to moving average filtering to eliminate sudden noise and outliers. Then, HMDE is used for the extraction of fault features. HMDE can evaluate the complexity of the signal at different levels and scales, thereby extracting more comprehensive information. Based on HMDE, entropy color block (ECB) images are generated and the improved HOG of the images are extracted. Finally, Knearest neighbor (KNN) is used to classify the improved HOG features, completing the recognition of different working states of rolling bearings. The validity and robustness of the proposed fault diagnosis framework are proved by the verification experiments on the public bearing datasets in Case Western Reserve University and Southeast University.

Method of intelligent agricultural pest image recognition based on machine vision algorithm

In recent years, people have paid increasing attention to smart agriculture. Its core technology is to combine information technology such as computer vision with agricultural production. In agricultural production, the occurrence of pests and diseases can seriously affect agricultural production. Therefore, in agricultural production, the diagnosis and identification of agricultural pests and diseases is the key to improve crop yield. Traditional machine vision methods often rely on feature extraction and classifier design, lack of strong learning and generalization ability, and can not effectively control crop diseases and pests in a timely manner. In order to solve this problem, this paper designs an agricultural pest recognition system based on machine vision algorithm of fuzzy recognition theory, and applies it to agricultural pest recognition. Compared with the traditional machine vision method, this method has obvious advantages, and can effectively improve the monitoring and early warning ability of crop diseases and pests. The experimental results show that the highest recognition rate of the machine vision optimization algorithm based on fuzzy recognition theory is 98.06%, and the lowest recognition error rate is 5.83%. It can be seen that the machine vision recognition algorithm based on fuzzy recognition theory has good recognition effect and can be used in agricultural pest recognition system to help farmers carry out agricultural production and reduce economic losses caused by diseases and pests.

Proposed methods of image recognition depend on the PCA

This paper suggest two method of recognition, these methods depend on the extraction of the feature of the principle component analysis when applied on the wavelet domain(multi-wavelet). First method, an idea of increasing the space of recognition, through calculating the eigenstructure of the diagonal sub-image details at five depths of wavelet transform is introduced. The effective eigen range selected here represent the base for image recognition. In second method, an idea of obtaining invariant wavelet space at all projections is presented. A new recursive from that represents invariant space of representing any image resolutions obtained from wavelet transform is adopted. In this way, all the major problems that effect the image and change its characteristic are solved through calculating invariant eigen range of the recursive resolution forms of all sub-images coefficient. These approaches employed here as multi-wavelet transform identifier with minimum Mahalanobis distance. All method recognition proposed in this paper are applied on different images. Different tables of image recognition resulted in accurate and fast.

Research on identification and detection of coal and gangue based on infrared thermal imaging technology and improved YOLOv8n

ABSTRACT Considering that traditional image recognition methods for coal and gangue are susceptible to environmental factors such as light and dust, this paper proposes an image recognition and detection method for the coal and gangue based on infrared thermal imaging technology and an improved YOLOv8n. First, we established a thermal infrared imaging platform for coal and gangue. We used the YOLOv8n model to analyze the differences between coal and gangue at various temperatures, determining the optimal temperature for effective recognition of the coal and gangue. Next, to facilitate the practical deployment of the detection model for coal and gangue, we incorporated the Ghost module into the original YOLOv8n model to reduce its complexity and storage requirements. Unfortunately, incorporating the Ghost module led to a slight decrease in the model’s detection performance. Next, we introduced the DAttention module into the model to enhance its feature extraction capabilities. At the same time, we replaced the CIoU loss function with DIoU, which further improved the model’s performance metrics. Finally, we compared the improved YOLOv8n model with Faster-RCNN model and SSD model to validate its feasibility and accuracy further and provide a new approach for the detection of coal and gangue.

YOLOv8-Coal: a coal-rock image recognition method based on improved YOLOv8.

To address issues such as misdetection and omission due to low light, image defocus, and worker occlusion in coal-rock image recognition, a new method called YOLOv8-Coal, based on YOLOv8, is introduced to enhance recognition accuracy and processing speed. The Deformable Convolution Network version 3 enhances object feature extraction by adjusting sampling positions with offsets and aligning them closely with the object's shape. The Polarized Self-Attention module in the feature fusion network emphasizes crucial features and suppresses unnecessary information to minimize irrelevant factors. Additionally, the lightweight C2fGhost module combines the strengths of GhostNet and the C2f module, further decreasing model parameters and computational load. The empirical findings indicate that YOLOv8-Coal has achieved substantial enhancements in all metrics on the coal rock image dataset. More precisely, the values for AP50, AP50:95, and AR50:95 were improved to 77.7%, 62.8%, and 75.0% respectively. In addition, optimal localization recall precision (oLRP) were decreased to 45.6%. In addition, the model parameters were decreased to 2.59M and the FLOPs were reduced to 6.9G. Finally, the size of the model weight file is a mere 5.2 MB. The enhanced algorithm's advantage is further demonstrated when compared to other commonly used algorithms.

A novel high-precision lightweight concrete bridge crack image recognition method based on knowledge distillation

A novel high-precision lightweight concrete bridge crack image recognition method based on knowledge distillation

Detection of coal gangue based on MSRCR algorithm and improved lightweight YOLOv8n

ABSTRACT Traditional methods for coal gangue sorting exhibit low efficiency, significant safety hazards, and limited applicability. Existing machine vision-based coal gangue image recognition methods struggle to balance model recognition speed and accuracy. In response to these challenges, this paper first utilizes the improved MSRCR algorithm to process images, enhancing the dark areas of coal gangue images while ensuring uniform brightness enhancement and image clarity. Furthermore, a novel lightweight coal gangue recognition method is proposed based on YOLOv8n, aiming to reduce data redundancy and improve recognition accuracy. Experimental results demonstrate that the improved lightweight model has a computational load of 7.1 GFLOPs, representing only 86.6% of the original model. The model detection rate is 73 fps, a 17 fps improvement over the original model. The accuracy, recall rate, and average precision reach 98.1%, 97.6%, and 98.9%, respectively, with improvements of 1.1, 0.9, and 0.5% points over the original model. The missing detection phenomenon is avoided effectively, and the accuracy and portability of the model are improved.

Defect recognition and classification from ultrasonic phased array total focusing method imaging based on domain adaption

Abstract With the rapid development of China’s economy, the demand for oil and gas resources is continuously increasing, leading to the expansion of the scale of oil and gas long-distance pipelines. Consequently, the risk of safety incidents in oil and gas pipelines is on the rise. Welding defects are identified as one of the significant factors contributing to safety incidents in long-distance oil and gas pipelines. In comparison to traditional conventional ultrasonic detection techniques, which exhibit low efficiency and accuracy, ultrasonic phased array detection technology has the capability to accurately detect welding defects in oil and gas long-distance pipelines. This is particularly true with the development of total focusing method imaging technology, enabling quantitative defect analysis and localization in oil and gas long-distance pipelines.However, in the current scenario, analyzing the types of defects in massive data from long-distance pipelines requires human intervention, leading to low detection efficiency and subjective influence on result analysis. Addressing challenges such as low efficiency in human evaluation of defects in ultrasonic phased array detection, low accuracy in traditional image recognition methods, strong subjectivity in manually extracting features, and the absence of a standardized industrial dataset for ultrasonic phased array detection defects, especially in the case of limited data on ultrasonic phased array detection of welding defects, this study focuses on small sample defects in phased array detection images. The study proposes an improved ResNet50 algorithm through domain adaptation in a deep neural network, trained on a large-scale model data, and then applied to ultrasonic phased array detection data. This approach achieves accurate identification and classification of typical volumetric defects (such as pores) and typical area defects (such as cracks) in the phased array detection of welding defects, laying the foundation for intelligent recognition of defects in long-distance oil and gas pipelines.

Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network

The current manual inspection of transmission line images captured by unmanned aerial vehicles (UAVs) is not only time-consuming and labor-intensive but also prone to high rates of false detections and missed inspections. With the development of artificial intelligence, deep learning-based image recognition methods can automatically detect various defect categories of transmission lines based on images captured by UAVs. However, existing methods are often constrained by incomplete feature extraction and imbalanced sample categories, which limit the precision of detection. To address these issues, a novel method based on multi-strategy image processing and an improved deep network is proposed to conduct defect diagnosis of transmission lines. Firstly, multi-strategy image processing is proposed to extract the effective area of transmission lines. Then, a generative adversarial network is employed to generate images of transmission lines to enhance the trained samples’ diversity. Finally, the deep network GoogLeNet is improved by superseding the original cross-entropy loss function with a focal loss function to achieve the deep feature extraction of images and defect diagnosis of transmission lines. An actual imbalance transmission line dataset including normal, broken strands, and loose strands is applied to validate the effectiveness of the proposed method. The experimental results, as well as contrastive analysis, reveal that the proposed method is suitable for recognizing defects of transmission lines.

Recognition of cross-regional loess water content based on machine vision: Case study of three regions

Robot technology equipped with vision system is expected to facilitate deep, in-hole, in-situ nondestructive recognition of water content in loess slopes. This advancement could considerably aid in the study of thespatial and temporal evolution of loess water content. However, a robust image recognition method capable of accurately recognizing the water content in loess across different regions is essential for the widespread adoption of this technology. Thus, we collected loess samples from the western (Lanzhou), central (Lantian), and eastern (Yan’an) parts of the Chinese Loess Plateau as a case study to explore the feasibility of a cross-regional intelligent recognition method for loess water content. Initially,we simulated the environmental conditions encountered during in-hole recognition to design an image collection platform, and subsequently prepared a loess water content dataset comprising 32,940 images from these three regions. Based on domain adaptation, we proposed a deep learning recognition method, namely, self-attention-based domain adaptation with Deep EXpectation (DA-DEX Swin). The proposed method demonstrates reasonable accuracy in estimating the water content of loess across regions, with a mean absolute error of 0.807%–1.137%, mean absolute percentage error of 0.074–0.154, and root mean square error of 0.939%–1.546%. The findings of this study provide valuable insights for addressing cross-regional issues, and DA-DEX Swin shows promise for application in robot detection technology to monitor variations and distributions of water content within slopes.

A pest image recognition method for long-tail distribution problem

A pest image recognition method for long-tail distribution problem

Research on the spatial quantity distribution of fibers in ultra high performance concrete slabs based on image recognition method

In this paper, the detection of fiber distribution in six UHPC slabs with fiber content of 1%, 2%, 3%, and horizontal pouring and vertical pouring is studied by image recognition method. The results show that: firstly, the fiber distribution can be identified by a series of processing steps, such as grayscale and binarization of fiber source image, extraction of initial detection area, extraction of fiber contour and segmentation of fiber adhesion area. Furthermore, it is proposed that the scaling curve of n-e detection area per pixel of fiber number can anchor the effective recognition area range of fiber. Secondly, the statistical analysis of the identification results can visually draw the fiber distribution maps of different types of UHPC slabs in different directions, and quantitatively characterize the fiber distribution uniformity of UHPC slabs by quoting the evaluation index of fiber distribution coefficient. Finally, the cross-sectional fiber Scatter Density Plot can perceptually show the degree of fiber dispersion and its position distribution. The more uniform the color, the more uniform the fiber distribution in the cross-section of the specimen.

Land use and soil property effects on aggregate stability assessed by three different slaking methods

AbstractAggregate stability is an important structural feature of soils, since it controls surface erosion, water infiltration, plant growth and carbon stabilisation. As such, it might be considered as a potential descriptor of soil health in repeated national to continental‐scale soil monitoring programmes, which is, as of now, rarely the case. This might be related to (i) the conception that it can be predicted reasonably well by standard soil parameters, and (ii) the lack of a high‐throughput method. Here, we used a paired plot approach with 50 cropland and adjacent grassland field margin plots to specifically test (i) if measuring aggregate stability is added value over its mere estimation based on soil properties, and (ii) if a high throughput image recognition method can bear comparison with more classical methods. We evaluated the mean weight diameter (MWD), water stable aggregates (WSA) using classical setups, as well as the slaking index (SI) via imagine recognition. Methods were compared regarding their sensitivity to considered parameters as well as their reproducibility. Soil organic carbon (SOC) as well as aggregate stability were significantly higher under grassland than under cropland soils. Remarkably, the specific design of the study could reveal that the difference in aggregate stability between land use types was not solely affected by SOC content and quality, derived from mid‐infrared spectroscopy. Quality and spatial distribution of organic matter inputs, absence of disturbance, as well as biotic parameters might all be relevant factors. Nevertheless, an important finding was that SOC quality had a higher explanatory power than SOC content alone for two out of three methods. Overall, the MWD was the most sensitive to the assessed drivers and together with the WSA the most reproducible method, with coefficients of variation below 6%. By contrast, those of the SI were as high as 192%, which hampered the detection of clear patterns along the soil property gradient and between land use types. For high‐quality scientific applications, 2D image recognition cannot be recommended. Instead, we recommend the use of the MWD or WSA method for scientific purposes with a low number of technical replicates in larger‐scale assessments to further unravel the importance of aggregate stability for healthy soils, and to better determine the underlying factors.

Iris image retrieval using partial matching of image blocks

AbstractThe problem of human identification through recognition of patterns in iris images captured in unconstrained environments results in image artefacts such as image occlusion and specular reflection, in which iris tissue is observable to extremely low content. To overcome this problem, this paper presents a novel method for iris image retrieval and recognition based on partial pattern matching. The main contribution of the proposed method relies on an image partitioning schema in which the iris is divided into non‐overlapping blocks with varying dimensions, facilitating the identification and removal of image regions impaired by eyelids, eyelashes, and specular reflections. In fact, the blocks that contain artefacts are completely ignored and those blocks are preserved that include useful patterns for identification. In addition, a multi‐feature similarity followed by a score fusion technique is employed for ranking the retrieval results. The remarkable results of the classification stage include an accuracy of 100%, 98.75%, and 99.94% on three benchmark databases, including UPOL, UBIRIS.V2, and CASIA‐Iris‐Interval.v3, respectively. Additionally, in the retrieval stage, the proposed method achieves a precision of 100% on all three benchmark databases, a recall of 83.33%, and a F1‐measure of 90.91 on the CASIA‐Iris‐Interval.v3 dataset.

Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification

Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification