In order to study the deeplearning model of urban planning image data processing and health intelligent system, based on existing remote sensing image change detection methods, the author introduces and proposes the ...
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In order to study the deeplearning model of urban planning image data processing and health intelligent system, based on existing remote sensing image change detection methods, the author introduces and proposes the use of deep belief networks in deeplearning to classify high-resolution remote sensing images and analyze urban expansion change detection. Compared with traditional methods, deeplearning has the highest overall accuracy and Kappa coefficient. deeplearning has the highest producer accuracy and relatively low misjudgment rate, making it the most suitable for studying the trend of urban built-up areas. By calculating the information entropy of the image to predict the number of hidden layer nodes, the time for deeplearning is greatly reduced. Under the same experimental conditions, the training time for each image can be shortened by 12 525 seconds has improved classification efficiency and made a significant contribution to research on urban expansion applications. Finally, the improved deep belief network was applied to classify and detect changes in the three phase remote sensing images of Beijing, and the urban expansion trend and characteristics of Beijing were analyzed. Provide technical reference and inspiration for urban planning and land use protection.
Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deeplearning-based crack image focuses more on cracks at a certain moment and ignores the full-time cra...
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Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deeplearning-based crack image focuses more on cracks at a certain moment and ignores the full-time crack expansion details, which are crucial for more reasonable evaluation and safety quantification of concrete structures. This paper proposes a new method based on the combination of improved You Only Look Once v7 (YOLOv7) algorithm, crack expansion benchmark method, improved deepLabv3+ algorithm, and image pro-cessing technology to monitor the whole process of crack development, including real-time crack recognition and real-time monitoring of crack dynamic expansion. The precision of the improved detection algorithm can be improved by a maximum of 5.34%, and the mean intersection over union (mIoU) of the improved segmentation algorithm can be improved by 0.15%, resulting in better segmentation results. The experimental results show that this method can efficiently and accurately achieve real-time tracking of crack dynamic expansion, especially for monitoring of tiny cracks.
Drilling is one of the most classical machining operations. real-time monitoring of drill wear can effectively testify whether the product fails to meet the specifications due to drill failure. This paper proposes a t...
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Drilling is one of the most classical machining operations. real-time monitoring of drill wear can effectively testify whether the product fails to meet the specifications due to drill failure. This paper proposes a tool wear assessment and life prediction model based on imageprocessing and deeplearning methods, which works effectively for small sample datasets and for low-quality images. The normal areas and worn areas of the drill bits are extracted using the U-Net network and traditional imageprocessing methods, respectively. Moreover, the original dataset is classified using the migration learning technique. The wear level of a drill bit can be accurately evaluated through experimental tests. Testing results show that the proposed method is more convenient and efficient than previous methods using manual measurements. These results can be applied to real-time drill wear monitoring, thus reducing part damage caused by tool wear.
In this research, we delve into advanced image segmentation techniques applied to drone imagery for various environmental and surveillance applications. By leveraging state-of-the-art models such as UNet, deepLabV3, M...
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ISBN:
(数字)9798331505073
ISBN:
(纸本)9798331505080
In this research, we delve into advanced image segmentation techniques applied to drone imagery for various environmental and surveillance applications. By leveraging state-of-the-art models such as UNet, deepLabV3, Manet, and Feature Pyramid Network (FPN), our goal is to achieve high precision in segmenting complex aerial scenes. Each of these models possesses unique strengths and weaknesses; hence, we employ an ensemble technique, weighted averaging, to harness their combined capabilities for superior results. Additionally, we incorporate image augmentation techniques to simulate various weather conditions such as haze and raindrops, enhancing the robustness of our models. To manage real-time data efficiently, we implement a streaming pipeline using Apache Kafka and Apache Spark, ensuring scalable and effective processing. Our methods demonstrate significant performance improvements when trained on the original dataset and the combination of original dataset and augmented dataset compared to conventional methods.
Machine vision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely imageprocessing, machine learning, and deep lea...
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Machine vision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely imageprocessing, machine learning, and deeplearning were investigated to detect and count infield corn kernels, immediately after harvest for combine harvester performance evaluation. A hand-held low-cost RGB camera was used to collect images with kernels of different backgrounds, based on which a 420 images dataset (200, 40, and 180 for training, validation, and testing, respectively) was generated. Three different models for kernel detection were constructed based on imageprocessing, machine learning, and deeplearning. For the imaging processing method, the images were preprocessed (color thresholding, graying, and erosion), followed by Hough circle detection to identify kernels. For the machine learning (cascade detector) and deeplearning (Mask R-CNN, EfficientDet, YOLOv5, and YOLOX), models were trained, validated, and tested. Experimental results showed the overall performance of the deeplearning network YOLOv5 was superior to the other approaches, with a small model size (89.3 MB) and a high model average precision (78.3 %) for object detection. The detection accuracy, undetection rate and F1 value were 90.7 %, 9.3 %, and 91.1 %, respectively, and the average detection rate was 55 fps. This study demonstrates that the YOLOv5 model has the potential to be used as a real-time, reliable, and robust method for infield corn kernel detection.
This paper provides an in-depth literature review on imageprocessing techniques, focusing on deeplearning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based...
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This paper provides an in-depth literature review on imageprocessing techniques, focusing on deeplearning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations for model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, and multimodal fusion of RGB and thermal data are also discussed. Finally, research gaps and opportunities are analyzed to create a holistic, scalable, and real-time inspection workflow for large-scale installation. This review serves as a reference for researchers and industry professionals to advance UAV-based PV inspection.
With the rapid development of artificial intelligence technologies, particularly deeplearning, the application of imageprocessing in emotion recognition and psychological therapy has become a growing area of researc...
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With the rapid development of artificial intelligence technologies, particularly deeplearning, the application of imageprocessing in emotion recognition and psychological therapy has become a growing area of research. As a crucial indicator of an individual's psychological state, accurate emotion recognition plays a vital role in psychological treatment and mental health management. Traditional emotion recognition methods primarily rely on subjective judgment by human experts, which has certain limitations. In contrast, deeplearning-based automated emotion recognition methods can capture emotional changes in real-time and with high accuracy through facial expressions, eye movement trajectories, and other image data, overcoming the shortcomings of traditional methods. Currently, emotion recognition technology is widely applied in fields such as psychological therapy, affective computing, and smart healthcare. However, existing research still faces challenges, including insufficient recognition accuracy, poor adaptability to individual differences, and weak integration with actual psychological therapy practices. In response to these issues, this paper proposes a deeplearning-based imageprocessing method that integrates multi-feature fusion techniques to improve the accuracy of emotion recognition. The method is applied to the detection of abnormal emotional states in psychological therapy and personalized emotion analysis. The results show that deeplearning technology can effectively recognize complex emotional changes and provide more accurate emotional intervention strategies for psychological therapy, offering significant theoretical and practical value.
Press brakes are among the most important machines used in sheet metal processing. In these machines, different numbers of molds are used for sheet bending and these molds are placed in the system by an operator. Howe...
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Press brakes are among the most important machines used in sheet metal processing. In these machines, different numbers of molds are used for sheet bending and these molds are placed in the system by an operator. However, this process is slow, error-prone, and dependent on human labor. In this study, a real-time system that automatically detects molds and manipulates a robotic arm was designed using YOLOv4 and imageprocessing. YOLOv4, a deeplearning (DL)-based object detection algorithm, was applied to detect the positions, types, and holes of molds. Classical imageprocessing methods were implemented to find the center (X, Y) coordinates of the mold hole. This study shows that the press brake machines currently used in industry can be transformed into smart machines through DL, imageprocessing, camera systems, and robotic arm features.
In this study, the development of a robotic cell that combines deeplearning and imageprocessing hybrid approach has been addressed in order to increase the accuracy and efficiency of the quality control of automotiv...
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In this study, the development of a robotic cell that combines deeplearning and imageprocessing hybrid approach has been addressed in order to increase the accuracy and efficiency of the quality control of automotive parts. In the automotive industry, manual quality control processes performed by operators are susceptible to errors and inaccuracies, leading to the passage of faulty parts and subsequent inefficiencies, wasted time, and increased costs. To overcome these challenges, this study introduces a fault detection robotic cell that combines deeplearning and imageprocessing techniques for quality control of automotive parts at Sahinkul Machine Spare Parts Manuf. Ltd. Co.. The robotic cell uses imageprocessing to inspect geometric tolerances, including hole diameter, part geometry and the presence of holes. However, the complex geometry of bolt threads requires the use of the YOLOv5 deeplearning algorithm to assess their quality. A dataset consisting of 3500 bolt thread images was collected for training and validation, with 2800 images used for training, 350 for validation, and the remaining 350 for testing purposes. The experimental results show that the fault detection robotic workcell achieves an approximate success rate of 97.4% in inspecting the quality of the selected parts. By combining deeplearning and imageprocessing, this study provides a reliable solution to improve the accuracy and efficiency of quality control processes in the automotive industry.
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