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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Comparison and Application of Vehicle Target Detection Methods Based on YOLOv5s and YOLOv7 7

Comparison and Application of Vehicle Target Detection Metho...

引用

7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Gu, Chenyu Du, Hong Zhang, Xiaozheng Li, Lidan Yang, Zhonglin Liu, Gaotian China Northern Vehicle Research Institute Beijing China

ISBN: (纸本)9798350350760

Currently, the methods for pedestrian detection have undergone significant expansion, yet vehicle target detection still retains immense potential for widespread application in the field of intelligent recognition. Nevertheless, vehicle target detection encounters challenges in terms of detection effectiveness, impacted by factors such as lighting conditions, environmental variations, and occlusions. This study centers on vehicle target detection, aiming to tackle the issues of difficult detection, easy missed detection, and algorithm selection within complex backgrounds. By utilizing the YOLOv5s and YOLOv7 algorithms, we have constructed a dataset that encompasses images featuring vehicles against complex backgrounds and in small scales. This dataset is then used to train the models, which are subsequently employed to perform target recognition for vehicles individually. Through comparative experimentation, we have proven the superiority of the YOLOv7 algorithm in detecting vehicles amidst complex backgrounds and those of small scales. ©2024 IEEE.

关键词： image processing vehicle target detection YOLOv5s YOLOv7

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A Fast image Mosaic Algorithm Based on Feature Partition Extraction

A Fast Image Mosaic Algorithm Based on Feature Partition Ext...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Zhiyou Lian Jianhua Ren School of Electronics and Information Engineering Liaoning University of Engineering and Technology Huludao Liaoning China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

This paper proposes a fast image stitching algorithm based on feature partitioning extraction to address the issues of long processing time, high computational complexity, and poor stitching performance in existing image stitching algorithms. This algorithm is implemented through two stages: optimizing image registration and image fusion. In the image registration stage, feature extraction and accumulation are carried out using image partitioning and an improved Gaussian pyramid layer series method; In the image fusion stage, an improved adaptive weighted average fusion algorithm is used for image fusion operations to improve stitching efficiency and make the image clearer and more natural. Experimental verification shows that the algorithm has strong robustness, significantly improving feature extraction speed and matching rate. Compared with mainstream algorithms, the stitching speed has increased by nearly 2 times, and the image related evaluation indicators have increased by about $5 \%$, basically meeting the real-time and timely needs of image stitching in daily life.

关键词： image registration Deformation Feature extraction Robustness Real-time systems Partitioning algorithms Computational complexity image stitching image fusion image reconstruction

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Green Intelligence: A Sequential CNN Odyssey in Mustard Leaf Disease Detection

Green Intelligence: A Sequential CNN Odyssey in Mustard Leaf...

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2024 ASU International conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Sakshi Sharma, Chetan Sharma, Shamneesh Sharma, Tushar Gochhait, Saikat School of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida India UpGrad Campus UpGrad Education Private Limited Karnataka Bangalore India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Sidtm Symbiosis International Deemed University Maharashtra Pune India Nri Samara State Medical University Russia

ISBN: (纸本)9798350372229

Mustard plants are a crucial agricultural commodity for food and oil production. However, they are frequently vulnerable to various diseases that can substantially reduce crop yield. Early identification and diagnosis of these illnesses are essential for efficient management and control, ensuring sustainable production and agricultural output continuity. This research presents a new method for categorizing mustard leaf diseases using Convolutional Neural Networks (CNNs). The study utilizes sophisticated machine learning algorithms to differentiate between healthy and diseased mustard leaves, which is crucial for maintaining agricultural output and guaranteeing food security. The study involves training CNN architectures - Sequential CNN, ResNet-50, VGG, and AlexNet - on a meticulously curated dataset comprising images of healthy and diseased mustard leaves. The classifier's effectiveness is validated through comprehensive testing, demonstrating significant precision, recall, and F1-score advancements over conventional methods. This approach provides an efficient tool for disease detection in mustard crops and contributes to sustainable agricultural practices, aligning with the global goal of food security and environmental sustainability. © 2024 IEEE.

关键词： Agricultural productivity Crops Data Augmentation image processing Precision Agriculture Sustainable Agriculture

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Smart Traffic Light Control System Using image processing

Smart Traffic Light Control System Using Image Processing

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2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Banu, Asha S.M Lakchida, Soundarya A S Shanthini, V.S. Stinsha, S.L. Sri Krishna College of Engineering and Technology Dept of ECE India

ISBN: (纸本)9781665489621

Crossing the road is one of the major problems, due to the increase of fast-moving vehicles on the road. The challenges in the existing techniques utilized for traffic control can be overcome by using the Smart Traffic Light Control System using image processing, proposed in this paper and allowing pedestrians to walk on busy roads conveniently. It is found that the Canny Edge Detection Technique is a very effective method among the various edge detection algorithms. The suggested method reduces the waiting time in zebra crossing by immediately controlling the traffic light and gives more time for elderly and handicapped pedestrians to cross the roads safely even during heavy traffic. When compared to all the traditional techniques, image processing is an efficient method for traffic control. This technique removes the usage of unwanted hardware like sensors which are used to sense noises. This prevents the wastage of time for vehicles which is more essential for emergency vehicles. The output of the code is obtained by image matching. The results are shown in three scenarios: less traffic, moderate traffic, and more traffic. © 2022 IEEE.

关键词： Roads and streets

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A Dual-Approach to Drone image Enhancement: Software-Based processing and FPGA Implementation

A Dual-Approach to Drone Image Enhancement: Software-Based P...

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Smart Electronics and Communication systems (ISENSE), International conference on

作者： Arun T Nair Nanditha N Varma Nandakumar R National Institute of Electronics and Information Technology Calicut

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work offers a thorough method for real-time dehazing of drone-captured images by different filtering techniques with post-processing improvements. Enhancing visibility and picture clarity in hazy situations is the main goal since it is essential for applications like environmental monitoring, navigation, and surveillance. In order to estimate the atmospheric light and transmission map, the suggested methodology makes use of the dark channel. A farrow filter and a guided filter are then applied to improve the transmission. Metrics including the PSNR, SSIM, Execution time, and MSE that are derived from Python-based image processing implementations are used to assess the effectiveness of the dehazing algorithms. The outcomes show notable gains in processing efficiency and image clarity. In order to accelerate the performance, this method was implemented on an FPGA based SoC. Power consumption and throughput of the FPGA - based solution were evaluated, demonstrating its effectiveness and appropriateness for real-time applications.

关键词： Surveillance Video sequences Random access memory Gray-scale Filtering algorithms Throughput Real-time systems System-on-chip Field programmable gate arrays Tuning

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Automatic conversion system of digital music score image based on image recognition algorithm 3

Automatic conversion system of digital music score image bas...

引用

3rd International conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Yao, Yi Xi'an Conservatory of Music Shaanxi Xi'an710000 China

ISBN: (纸本)9781510661240

The development of target detection and recognition algorithms in the field of image processing has promoted the development of automatic image conversion systems for digital musical scores and related algorithms. This was crucial for the development of note-recognition coding and the conversion of musical scores into readable digital formats. The purpose of this paper is to study the automatic conversion system of digital music score image based on image recognition algorithm. The deep learning technology based on computer vision is used for musical score image recognition, and a digital musical score image automatic conversion system is designed and built. Harmonic peak method and confidence method are introduced, and their shortcomings are analyzed. Combined with the advantages of these two algorithms, an improved normal harmonic method is proposed, and the detailed ideas and steps of the algorithm are given. And the extracted fundamental frequency and time information are converted into pitch and time value combined with the basic knowledge of music theory. The recognition accuracy rate of the normal harmonic method proposed in this paper reaches more than 92%, which not only greatly improves the recognition accuracy, but also retains the strong anti-noise characteristics of the traditional harmonic peak method. © 2022 SPIE.

关键词： image recognition

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Application Of MLOps Practices For Biomedical image Classification 5

Application Of MLOps Practices For Biomedical Image Classifi...

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5th International conference on Informatics and Data-Driven Medicine (IDDM)

作者： Berezsky, Oleh Pitsun, Oleh Melnyk, Grygory Batko, Yuriy Derysh, Bohdan Liashchynskyi, Petro West Ukrainian Natl Univ 11 Lvivska St UA-46001 Ternopol Ukraine

With active hardware development, the number of software machine learning-based systems has increased dramatically in all areas of human activity, in particular, in medicine. The use of machine learning elements in software systems requires the organization of a pipeline process of software development, testing, and support. The application of MLOps technologies will improve the quality and speed of system development, as well as simplify the process of adjusting the algorithm parameters to improve the system operation quality. The purpose of this work is to develop an MLOps pipeline that will consider all the necessary stages of software development based on machine learning algorithms for biomedical image processing.

关键词： Machine learning MLOps biomedical images programming

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Retinal image Segmentation U sing Clustering Methods: Performance Analysis 19

Retinal Image Segmentation U sing Clustering Methods: Perfor...

引用

19th IEEE International Multi-conference on systems, Signals and Devices, SSD 2022

作者： Mehidi, Imane Jabri, Dalel Belkhiat, Djamel Eddine Chouaib Ferhat Abbas University Dac Hr Laboratory Setif Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Clustering is a popular method for seg-menting retinal images due to its effectiveness in performance. This paper investigates the ability of multiple clustering algorithms to segment the retinal image to isolate blood vessels. The flowchart of this analyzes two phases. First, the pre-processing phase aims to increase the contrast of the image by applying a series of operations: Contrast Limited Adaptive Histogram Equalization (CLAHE), median filtering, and bottom-hat filtering. Furthermore, the second phase deals with the segmentation of the pre-processed image using one of the clustering algorithms selected for this study: k-means (KM), k-medoids (KD), Gaussian Mixture Model (GMM), and fuzzy c-means (FCM). The experimental part is carried out on the DRIVE and STARE databases, and the performance measure of each algorithm is accomplished using sensitivity, specificity, and accu-racy performance metrics. In addition, the running times of different algorithms are compared. Finally, the obtained results are extensively discussed, and some suggestions are proposed at the end of this paper. © 2022 IEEE.

关键词： Blood vessels

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Development of an Urban Vegetation Monitoring System using image processing and Deep Learning 10

Development of an Urban Vegetation Monitoring System using I...

引用

10th IEEE International conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Alighaleh, Pejman Ooi, Melanie Kaung, Ye Chow University of Waikato School of Engineering Hamilton New Zealand

ISBN: (纸本)9798350365283

Automated monitoring of urban vegetation, particularly trees, facilitates large-scale urban planning and environmental surveillance. However, such systems can be cost-prohibitive to deploy, especially in smaller cities. This paper describes a cost-efficient standalone RGB-imaging platform with adaptable mounting for service vehicles, implemented in Hamilton East, Hamilton City, New Zealand. First, the hardware design and imaging platform are described. Then, to evaluate the quality of the tree imagery captured by this system, two deep-learning models, YOLOv5n and YOLOv8n, were trained using the collected images. The collected dataset consists of approximately 12,000 images of various tree species typical to the region. Of these, 450 images (4%) were used for training and validation, and the remaining images were used for testing. The models were then tested on images collected in 4 different test image collection sessions. The results of the models in the validation phase showed that the YOLOv5n model demonstrated a precision of 0.87 and a recall of 0.85, compared to YOLOv8n's precision of 0.86 and recall of 0.82. During the test phase, both models performed comparably, indicating that the imaging system can capture high-quality images before direct use with deep-learning algorithms for tree detection. Furthermore, the imaging system is capable of capturing at least 11 images per tree on an urban route, showing the potential to be deployed for urban vegetation monitoring. © 2024 IEEE.

关键词： Imaging systems

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Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation 48

Secure and Decentralized Collaboration in Oncology: A Blockc...

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48th Annual IEEE International Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Ranjbarzadeh, Ramin Keles, Ayse Crane, Martin Anari, Shokofeh Bendechache, Malika Dublin City Univ Fac Engn & Comp Sch Comp Dublin Ireland Ankara Medipol Univ Fac Engn & Nat Sci Dept Artificial Intelligence Engn Ankara Turkiye Islamic Azad Univ South Tehran Branch Dept Accounting Econ & Financial Sci Tehran Iran Univ Galway ADAPT Res Ctr Sch Comp Sci Galway Ireland

ISBN: (纸本)9798350376975;9798350376968

This research presents an innovative framework that uses blockchain technology to improve tumor segmentation in medical imaging. The approach tackles issues related to data security, particularly when dealing with real private dataset, annotation accuracy, and collaboration. With the growing reliance of the medical industry on accurate tumor segmentation from medical images for cancer diagnosis and treatment, current methods are inadequate in maintaining data accuracy and promoting collaboration among experts across different countries. Our suggested approach utilizes blockchain technology to establish a decentralized, secure platform for the collaborative obtaining, annotation, and validation of medical images by data scientists, oncologists, and radiologists. Smart contracts streamline essential procedures such as verification of annotations, consensus among experts, and remuneration of contributors, guaranteeing the dependability and excellence of the data. Furthermore, the unchangeable record of transactions in the blockchain ensures a reliable basis for implementing artificial intelligence and machine learning algorithms. This improves the accuracy of segmenting data and allows for predictive modeling. This strategy not only improves the precision and effectiveness of tumor segmentation but also promotes a worldwide collaborative environment, which has the potential to revolutionize cancer diagnostics and treatment planning. Furthermore, it ensures the privacy and security of patient data.

关键词： Blockchain Technology Decentralized Healthcare systems Collaborative Annotation Medical image processing Data Security and Privacy

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