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Computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Zhicheng Tang Yuxin Zhu Mei Huang Junke Zhu Shandong University of Technology Zibo China Shandong Agricultural University Taian China Qilu Normal University Jinan China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

In this paper, the application of machine vision and deep learning technologies in smart agriculture, particularly in crop disease and pest identification, crop growth monitoring, and crop lodging detection is explored. By utilizing visible light images of wheat canopies obtained from drones, a deep learning model tailored for low-light conditions has been constructed. This model integrates Transformer networks and Mamba networks, gradually improving image quality and recognition accuracy through multi-layer processing. The experimental results show that this model achieves outstanding performance in wheat recognition tasks, with the precision (precision) reaching 0.831, the mean intersection over union (Miou) reaching 0.761, the recall reaching 0.751, and F1 score reaching 0.821, significantly outperforming other classical models. These results demonstrate the effectiveness and superiority of the model in practical applications.

关键词： Smart agriculture Deep learning image quality image recognition Accuracy machine vision Crops Transformers Robustness Monitoring

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image processing and Optimization for Higher Order Aberration Correction and Power Distribution Management

Image Processing and Optimization for Higher Order Aberratio...

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作者： Olga Jumbo Sanchez University of Miami

学位级别：博士

Optimization and machine learning are two of the most important developments in modern computational science. Over the years, research integrating these areas has proven to advance technology and find key insights for many applications. The interplay between these two areas has enjoyed prominence due to their ability to model real-life applications and designing algorithms that process large volumes of data at fast speeds. One type of data that has gained attention in the past years are images. With an improvement in storage and processing capabilities, researchers are studying techniques to extract important features from two-dimensional information. Hence, this dissertation focuses on developing new algorithms that incorporate concepts from optimization and machine learning fields. Particularly, this dissertation presents novel techniques that cater effective solutions for two applications: Higher Order Aberration correction and power distribution management. In the first application, mathematical programming and image processing are utilized to improve vision for people with visual deformities. In the second application, machine learning and optimization are utilized to monitor and maintain vegetation surrounding the power grid. This dissertation extensively researches the interaction of image processing, deep learning, and mathematical programming for developing cost-effective solutions with higher accuracy and lower computational complexity. Furthermore, the proposed methodologies illustrate the importance of multidisciplinary research and in-depth understanding of the literature across fields. These methodologies bridge the gap and act as an interface between electrical and industrial engineering and provide an outlet for developing creative and innovative solutions.

关键词： image processing Powergrid Management Mathematical Programming machine Learning Ophthalmology Optimization

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AI for Space Operations: the next generation of Mission Operations for Earth Observation constellations 74

AI for Space Operations: the next generation of Mission Oper...

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74th International Astronautical Congress, IAC 2023

作者： Schandeler, Baptiste Lamothe, Matthieu Chouteau, Florient Future Programmes Department Airbus Defence and Space 31 rue des cosmonautes Toulouse31400 France Image and Mission Engineering Department Airbus Defence and Space 31 rue des cosmonautes Toulouse31400 France Computer Vision & Advanced Data Processing Department Airbus Defence and Space 31 rue des cosmonautes Toulouse31400 France

Earth Observation Satellite constellations are more and more requested by customers to fit their strategic needs. They offer the ability to collect large amounts of data from various places, from various sensors. They offer versatility, robustness and an undeniable strategic advantage of revisit. Classical way of operations, where every satellite, subsystems and the ground segment were operated by a team, does fit neither technically nor commercially for constellations. Artificial intelligence (AI) has proven its value in multiple applications. Automated mission operations, especially in multi-missions context, have a high impact in terms of efficiency where reduction of human interactions is foreseen: routine operations, monitoring, data processing and ground segment health. Airbus has already developed automated collision avoidance maneuver (CAM), automated mission plan uplink and advanced monitoring that already allow us to operate with a limited number of operators. AI-based image production and analysis is one of the latest developments Airbus has performed: change detection and Deepzoom are now available on the market. AI is set to play a major role in the automation of future EO systems, enabled by the significant advances in machine Learning techniques of recent years. Data-driven AI-based solutions can be developed to improve operations effectiveness. Multi-variable multi-subsystems analysis (based on neural networks) for time series can be used to predict future behaviors. Identifying a potential upcoming failure before it occurs and proposing preventive procedures to reduce the downtime is one of the leaps where the classical way of operations is outdated. The spacecraft can no longer be controlled individually by operators: the impact of on-board AI will drastically shake current CONOPS. Today, Airbus is working on artificial intelligence concepts for the next-gen EO constellations: AI on board and on ground to reach the lights-out center, where AI and a

关键词： Artificial Intelligence Constellations Earth Observation machine Learning Satellite operations

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A New Video Summarization Approach Using Object Density Based image Similarity for Smart City applications

A New Video Summarization Approach Using Object Density Base...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Turan Goktug Altundogan Mehmet Karakose Fatih Mert Computer Engineering Department Manisa Celal Bayar University Manisa Türkiye Computer Engineering Department Firat University Elazig Türkiye Huawei Telecommunication Foreign Trade Co. Ltd. Istanbul Türkiye

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Summarization approaches are currently proposed solutions that focus on meaningfully reducing different types of data such as text, audio, and video. Many techniques such as machine learning, signal processing, image processing, computer vision, and deep learning can be used to develop summarization approaches. In this study, we performed object detection on videos that can be used in smart city applications using a pretrained YOLOv8 model. As a result of the object detection, we created a feature vector for each image frame by using the location information covered by the classes used in the object detection process. Then, we used several different approaches to determine the reference feature vector for the video. Finally, we calculated the cosine similarities of the feature vector for each frame to this reference feature vector using different methods. With the method we developed, we presented a similarity-focused summary created by selecting the video frames expressed with maximum similarity. We also developed an evaluation approach to evaluate the summaries we presented, comparing the overall heat maps of the video with the heat maps of the summary videos. Experimental results demonstrate the efficiency of our summarization approaches.

关键词： Deep learning Computer vision Smart cities Computational modeling Object detection Signal processing Feature extraction Vectors Heat maps Information technology

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Semantic Segmentation of Multi-Channel Polycrystalline Structure Micrographs Using Convolutional Neural Networks 20

Semantic Segmentation of Multi-Channel Polycrystalline Struc...

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20th IEEE International Conference on machine Learning and applications (ICMLA)

作者： Selmaier, Andreas Lutz, Benjamin Kisskalt, Dominik Boernicke, Simon Fuerst, Jens Franke, Joerg Friedrich Alexander Univ Erlangen Nurnberg Inst Factory Automat & Prod Syst Erlangen Germany Siemens Healthineers Technol Ctr Power & Vaccuum Components Erlangen Germany

ISBN: (纸本)9781665443371

Due to ever-increasing data availability, computational power, and algorithmic advances, machine learning enables various novel industrial applications. A field with particularly strong potential for machine learning is computer vision. As part of that area, semantic segmentation refers to a process that links each pixel of an image to a corresponding class. In this task, deep learning has outperformed traditional image processing techniques as well as other classical machine learning techniques and therefore has become the new standard approach. Convolutional neural networks (CNN) are a specific form of deep neural networks, which exploit spatial information for the classification of pixels. This paper presents an approach in which CNNs are optimized and applied to a semantic segmentation problem by using multichannel microscopic images of tungsten flat emitters to determine their surface conditions. These are of strong interest since they may reveal information about potential defects and the life expectancy of the emitter, which is an indispensable component of X-ray tubes as they are used in medical applications.

关键词： manufacturing machine learning computer vision semantic segmentation visual inspection X-ray flat emitters medical devices

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machine vision Inspection of Object Positioning in a Hard Disk Assembly Plant

Machine Vision Inspection of Object Positioning in a Hard Di...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Wichian Ooppakaew Boonyarit Kumkiet Jakkree Srinonchat dept. of electronics and telecommunication engineering Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani Thailand

machine vision is a field of computer vision that focuses on developing and implementing automated visual inspection systems. It involves using cameras, sensors, and image processing algorithms to capture and analyze images or video data for various applications. machine vision enables object detection, recognition, measurement, and quality control, enhancing industry efficiency and accuracy, mainly focusing on object positioning inspection in this research. Object positioning inspection using machine vision has become critical in various industrial applications requiring precise object alignment and positioning. This review explores the fundamental principles, challenges, and applications of object positioning inspection, including detecting misalignment, rotation, translation, and scaling. The proposed primary objective of object positioning inspection is to ensure that objects are correctly positioned with reference points or predefined geometric parameters applied in the hard disk component assembly factory. The edge detection and decision function are operated with 1,000 images captured from the real-time camera. The experiment result indicated that the average accuracy is 95.27% for object positioning inspection. Additionally, this proposal identifies current research trends and future directions for advancing object positioning inspection using machine vision for industrial applications.

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Real-Time Semantic Segmentation via Spatial-Detail Guided Context Propagation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第3期PP卷 4042-4053页

作者： Hao, Shijie Zhou, Yuan Guo, Yanrong Hong, Richang Cheng, Jun Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Chinese Acad Sci CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen Inst Adv Technol Beijing 100864 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g., semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the spatial-detail guided context propagation network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Cityscapes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768 x 1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters. The code will be released at https://***/zhouyuan888888/SGCPNet.

关键词： Semantics Computational modeling Convolution Pipelines Costs Real-time systems image segmentation Accuracy contextual information deep learning semantic segmentation speed

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machine Learning Model Interpretability in NLP and Computer vision applications 5th

Machine Learning Model Interpretability in NLP and Computer ...

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5th International Conference on Advances in Computing and Data Sciences (ICACDS)

作者： Chakrabarty, Navoneel Int Inst Informat Technol IIIT Bangalore Bengaluru India

ISBN: (纸本)9783030814625;9783030814618

In light of the recent advancements in Artificial Intelligence (AI), the application of machine Learning in the domains of Natural Language processing and Computer vision is increasing by leaps and bounds. Deployment of machine Learning Models in applications (apps) have been rampant with the aim of achieving automation, mainly involving textual and image data. Textual data indulges the subject of Text Analytics (also known as NLP) into action and image data indulges the subject of Computer vision into play. But, the performance of machine Learning in the domains of Text Analytics or vision needs to be judged before deployment. Now, performance analysis of ML Models are done with the help of performance metrics, most importantly AUC Score in Classification Problems, but justification by means of numerical scores only, can't establish the relevance of the Model Performance with Domain Knowledge. In this paper, 1 standard NLP use-case and 4 Computer vision use-cases are considered for ML Model Interpretability Enhancement that can throw light on the relevance with the concerned Domain Knowledge, the use-case deals with.

关键词： Model Interpretability NLP Computer vision Wordcloud Feature Map Visualization

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High-speed object detection with a single-photon time-of-flight image sensor

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OPTICS EXPRESS 2021年第21期29卷 33184-33196页

作者： Mora-Martin, German Turpin, Alex Ruget, Alice Halimi, Abderrahim Henderson, Robert Leach, Jonathan Gyongy, Istvan Univ Edinburgh Sch Engn Inst Integrated Micro & Nano Syst Edinburgh EH9 3FF Midlothian Scotland Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland Barcelona Inst Sci & Technol ICFO Inst Ciencies Foton Barcelona 08860 Spain Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland

3D time-of-flight (ToF) imaging is used in a variety of applications such as augmented reality (AR), computer interfaces, robotics and autonomous systems. Single-photon avalanche diodes (SPADs) are one of the enabling technologies providing accurate depth data even over long ranges. By developing SPADs in array format with integrated processing combined with pulsed, flood-type illumination, high-speed 3D capture is possible. However, array sizes tend to be relatively small, limiting the lateral resolution of the resulting depth maps and, consequently, the information that can be extracted from the image for applications such as object detection. In this paper, we demonstrate that these limitations can be overcome through the use of convolutional neural networks (CNNs) for high-performance object detection. We present outdoor results from a portable SPAD camera system that outputs 16-bin photon timing histograms with 64x32 spatial resolution, with each histogram containing thousands of photons. The results, obtained with exposure times down to 2 ms (equivalent to 500 FPS) and in signal-to-background (SBR) ratios as low as 0.05, point to the advantages of providing the CNN with full histogram data rather than point clouds alone. Alternatively, a combination of point cloud and active intensity data may be used as input, for a similar level of performance. In either case, the GPU-accelerated processing time is less than 1 ms per frame, leading to an overall latency (image acquisition plus processing) in the millisecond range, making the results relevant for safety-critical computer vision applications which would benefit from faster than human reaction times. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License.

关键词： Diode lasers image sensors machine vision Neural networks Object detection Spatial resolution

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On detailed deviation zone evaluation of scanned surfaces for automatic detection of defected regions

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MEASUREMENT 2023年第1期221卷

作者： Kalajahi, Elnaz Ghanbary Mahboubkhah, Mehran Barari, Ahmad Univ Tabriz Dept Mech Engn Bahman 29th Blvd Tabriz *** Iran Univ Ontario Inst Technol Dept Mech & Mfg Engn Oshawa ON Canada

Online inspection and surface characterization including 3D defect detection of surfaces using 3D scanning and coordinate metrology data has crucial applications in today's Industry 4.0. Although 3D vision-based metrology methods are superior to 2D in providing spatial information, its processing remains challenge. A novel automated Detailed Deviation Zone Evaluation method is proposed in this paper in which 3D unorganized PC is converted into 2D grayscale image such that the intensity variation of image is proportional to the surface topography. This developed image is addressed as "skin image" of the scanned surface. The considered point clouds include only XYZ-coordinates. No prior knowledge of defects, and no training set is required. The methodology is fully implemented, and verified by inspecting point clouds of several workpieces with predefined defects. The experimental results show high efficiency of the developed methodology in defect detection and 3D-feature identification irrespective of shape and size.

关键词： Skin method Deviation zone evaluation Point cloud segmentation 3D defect detection and characterization machine vision Industry 4.0

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