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real-time monitoring of concrete crack based on deep learning algorithms and image processing techniques

ADVANCED ENGINEERING INFORMATICS 2023年 58卷

作者： Xu, Gang Yue, Qingrui Liu, Xiaogang Xian Univ Architecture & Technol Coll Civil Engn Xian 710055 Shaanxi Peoples R China Univ Sci & Technol Beijing Sch Civil & Resource Engn Res Inst Urbanizat & Urban Safety Beijing 100083 Peoples R China

Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deep learning-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.

关键词： Crack monitoring Improved YOLOv7 algorithm Improved deepLabv3+algorithm Crack expansion benchmark

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Tool wear assessment and life prediction model based on image processing and deep learning

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期126卷 1303-1315页

作者： Wu, Cheng Wang, Shenlong Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China

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 image processing and deep learning 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 image processing 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.

关键词： Drill bit wear Wear assessment image processing U-Net Migration learning

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A real-time Drone image processing System using deep learning and Big Data Technology

A Real-Time Drone Image Processing System using Deep Learnin...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Anh-Tuan Nguyen D. Trong-Hop Do University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

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.

关键词： deep learning image segmentation Surveillance Pipelines Big Data Streaming media real-time systems Robustness Drones Meteorology

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Infield corn kernel detection using image processing, machine learning, and deep learning methodologies under natural lighting

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartE期238卷

作者： Liu, Xiaohang Zhang, Zhao Igathinathane, C. Paulo, Flores Zhang, Man Li, Han Han, Xiongzhe Ha, Tuan Yiannis, Ampatzidis Hak-Jin, Kim Minist Educ Key Lab Smart Agr Syst Integrat Beijing 100083 Peoples R China China Agr Univ Key Lab Agr Informat Acquisit Technol Minist Agr & Rural Affairs Beijing 100083 Peoples R China North Dakota State Univ Dept Agr & Biosyst Engn Fargo ND 58102 USA Kangwon Natl Univ Coll Agr & Life Sci Dept Biosyst Engn Chunchon 24341 South Korea Kangwon Natl Univ Coll Agr & Life Sci Interdisciplinary Program Smart Agr Chunchon South Korea Thai Nguyen Univ Agr & Forestry Tuan M Ha Hitech Agriculture& Forestry R&D Ctr Thai Nguyen City 24119 Vietnam Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept 2685 FL-29 Immokalee FL 34142 USA Seoul Natl Univ Coll Agr & Life Sci Dept Biosyst Engn Seoul South Korea Seoul Natl Univ Coll Agr & Life Sci Convergence Major Global Smart Farm Seoul South Korea

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 image processing, machine learning, and deep learning 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 image processing, machine learning, and deep learning. 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 deep learning (Mask R-CNN, EfficientDet, YOLOv5, and YOLOX), models were trained, validated, and tested. Experimental results showed the overall performance of the deep learning 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.

关键词： Infield corn kernel Object detection image processing Machine learning deep learning

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Towards a Holistic Approach for UAV-Based Large-Scale Photovoltaic Inspection: A Review on deep learning and image processing Techniques

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TECHNOLOGIES 2025年第3期13卷 117-117页

作者： Barraz, Zoubir Sebari, Imane El Kadi, Kenza Ait Abdelmoula, Ibtihal Ait IAV Hassan II Sch Geomat & Surveying Engn Photogrammetry Cartog Dept Geospatial Technol Smart Decis Res Unit Rabat 10101 Morocco IRESEN GEP UM6P Green Energy Pk Res Platform BenGuerir 43150 Morocco

This paper provides an in-depth literature review on image processing techniques, focusing on deep learning 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.

关键词： anomaly classification deep learning holistic approach image analysis large-scale photovoltaic installations inspection photovoltaic module segmentation UAV images

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Application of deep learning-Based image processing in Emotion Recognition and Psychological Therapy

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TRAITEMENT DU SIGNAL 2024年第6期41卷 2923-2933页

作者： Liu, Yang Zhang, Yawen Wang, Yuan Xinjiang Normal Univ Sch Educ Sci Urumqi 830017 Peoples R China Xinjiang Key Lab Mental Dev & Learning Sci Urumqi 830017 Peoples R China Xinjiang Normal Univ Ctr Xinjiang Higher Educ Dev Studies Urumqi 830017 Peoples R China

With the rapid development of artificial intelligence technologies, particularly deep learning, the application of image processing 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, deep learning-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 deep learning-based image processing 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 deep learning technology can effectively recognize complex emotional changes and provide more accurate emotional intervention strategies for psychological therapy, offering significant theoretical and practical value.

关键词： deep learning image processing emotion recognition psychological therapy multi-feature fusion personalized analysis

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An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2023年第8期37卷 4239-4247页

作者： Ozic, Muhammet Usame Barstugan, Mucahid Ozdamar, Atakan Pamukkale Univ Fac Technol Dept Biomed Engn Denizli Turkiye Konya Tech Univ Fac Engn & Nat Sci Dept Elect Elect Engn Konya Turkiye MVD Machinery Ind Inc Res & Dev Ctr R&D Konya Turkiye

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 image processing. YOLOv4, a deep learning (DL)-based object detection algorithm, was applied to detect the positions, types, and holes of molds. Classical image processing 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, image processing, camera systems, and robotic arm features.

关键词： deep learning image processing Mold Press brake YOLOv4

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Illuminating Solar Potential: A real-time deep learning, IoT, and image processing Approach to Ground-Based Cloud Movement Forecasting for Enhanced Solar Energy Management

Illuminating Solar Potential: A Real-Time Deep Learning, IoT...

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Women in Computing (InCoWoCo), International Conference for

作者： P.K.M. Perera K.B.S. Karunarathna S. Hewawalpita S.M. Dassanayake Department of Decision Science Faculty of Business University of Moratuwa Sri Lanka

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Cloud movement impacts the performance of photovoltaic (PV) power plants by causing sudden fluctuations in output power, leading to voltage instability in connected electricity networks. This paper introduces a novel and cost-effective deep learning solution for forecasting shortterm cloud movement to regulate PV power output. The methodology involves capturing raw image data through a PyCamera connected to a Raspberry Pi, followed by rigorous pre-processing and cloud detection techniques. Using colourbased segmentation, morphological operations, and contour analysis, the developed pipeline accurately identifies cloud regions in images and predicts their movement for the subsequent time interval. The proposed algorithm is designed to be efficient, lightweight, and suitable for deployment on low-cost processors. This study successfully implements and optimizes a cloud detection system for resource-constrained environments using the Raspberry Pi 4 Model B. The method significantly improves inference speed and model efficiency while addressing the high latency constraints associated with embedded devices with execution periods measured in seconds. The model performance metrics validate the approach, with an accuracy of 89.84%, indicating the practical potential of the model for real-time applications, offering a promising solution towards the integration of effective cloud forecasting into cost-sensitive energy management systems.

关键词： Cloud computing image segmentation Adaptation models Computational modeling Pipelines Predictive models real-time systems Sun Forecasting Morphological operations

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deep learning- and image processing-based methods for automatic estimation of leaf herbivore damage

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METHODS IN ECOLOGY AND EVOLUTION 2024年第4期15卷 732-743页

作者： Wang, Zihui Jiang, Yuan Diallo, Abdoulaye Banire Kembel, Steven W. Univ Quebec Montreal Dept Sci Biol Montreal PQ Canada Sun Yat Sen Univ Sch Ecol Guangzhou Peoples R China Univ Quebec Montreal Dept Informat Montreal PQ Canada

Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time-consuming which limits the amount of data that can be collected and prevent answering big picture questions that require large-scale sampling of herbivory pressure. Recent developments in deep learning have provided a potential tool for automatic collection of ecological data from various sources. However, most applications have focused on identification and counting, and there is a lack of deep learning tools for quantitative estimation of leaf herbivore damage. Here, we trained generative adversarial networks (GANs) to predict the intact status of damaged leaves and applied image processing technique to estimate the area and percentage of leaf damage. We first described procedures for collecting leaf images, training GAN models, predicting intact leaves and calculating leaf area, with a Python package provided to enable hands-on application of these procedures. Then, we collected a large leaf data set to train a universal deep learning model and developed an online app HerbiEstim to allow direct use of pretrained models to estimate herbivory damage of leaves. We tested these methods using both simulated and real leaf damage data. The procedures provided in our study greatly improved the efficiency of leaf herbivore damage estimation. Our test demonstrated that the reconstruction of damaged leaf image resembled the ground-truth image with a similarity of 98.8%. The estimation of leaf herbivore damage exhibited a high accuracy with an averaged root mean square error of 1.6% and had a general applicability to different plant taxa and leaf shapes. Overall, our work demonstrated the feasibility of applying deep learning techniques to quantify leaf herbivory intensity. The use of GANs allows automatic e

关键词： data collection deep learning generative adversarial network image processing leaf herbivore damage

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image processing and Visualization Technology Application of an Improved deep learning Algorithm 2

Image Processing and Visualization Technology Application of...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Deng, Yunsheng Huang, Yinghui Chen, Zhangbao Chen, Yan Bengbu University Bengbu Anhui Huainan City China

ISBN: (纸本)9798350372892

In today's world, technology is changing our way of life and work at an alarming rate. This paper studies the performance of an improved deep learning algorithm in image processing tasks, introduces the implementation principle of algorithm design, and puts forward an improved deep learning algorithm. In the experimental method part, a group of experiments are designed to evaluate the image quality performance of the improved deep learning algorithm, which is based on three key performance indicators: Peak Signal-to-Noise Ratio (PSNR), Interactive Response time (IRT) and Structural Similarity Index Measure (SSIM). The research conclusion shows that the peak signal-to-noise ratio (PSNR) of the improved image quality-preserving deep learning algorithm is as high as 58 dB. The maximum IRT measurement of the improved algorithm is only 95 ms, which provides users with faster response speed and enables users to experience a smoother interactive experience in real-time image processing applications. © 2024 IEEE.

关键词： Contrastive learning

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