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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Santhosh D S. Prabu Department of Computer Applications Hindustan Institution of Technology and Science Chennai Tamil Nadu

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Traffic surveillance is a key factor in ITS whereby accurate and real-time object detection assures improvement of road safety and traffic management. This paper advances a deep-learning-based perspective that combines image processing techniques with convolutional neural networks (CNNs) to maximize the object detection accuracy over the traffic camera feed. The filtering, edge detection, and feature extraction of images for data preprocessing enhance the model performance. This setup provides high inference speed with reliable detection of vehicles via YOLO (You Only Look Once) and Faster R-CNN. The obtained experimental results from our evaluation show great enhancement in the detection accuracy which places the model in a position for real-time application in traffic surveillance. The study also investigates the computational efficiency and real-life boundaries for implementing this strategy, providing an exhaustive account of the suitability of the proposed method in larger-scale applications in ITS.

关键词： YOLO deep learning Computer vision Accuracy Surveillance Computational modeling Traffic control Cameras real-time systems Convolutional neural networks

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Enhanced image processing for Automobile Accident Detection Using deep learning 3

Enhanced Image Processing for Automobile Accident Detection ...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Usharani, S. Dhanushkumar, J. Manju Bala, P. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning Villupuram India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once,"is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times. © 2024 IEEE.

关键词： image enhancement

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Automatic Volume Control Using image processing and deep learning Techniques: A Review

Automatic Volume Control Using Image Processing and Deep Lea...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Kinshuk Theraja Sakshi Gupta deepak Kumar Gupta Amity Institute of Information and Technology Amity University Noida

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This paper presents an innovative approach to automatic volume control using image processing and deep learning techniques. The ability to automatically adjust volume levels based on environmental factors and user preferences has significant implications for various audio applications, including teleconferencing systems, smart devices, and public address systems. By combining image processing algorithms with deep learning models, this paper aims to develop a robust and adaptive volume control system capable of accurately adjusting audio levels in real-time. The paper discusses the theoretical foundations, technical implementation, experimental results, and potential applications of the proposed automatic volume control system.

关键词： deep learning Visualization Teleconferencing Solid modeling image processing Software algorithms Process control

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Research on rail surface crack identification and detection based on deep learning and image processing 2

Research on rail surface crack identification and detection ...

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2nd International Conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Yu, Wang Rong, Miaobing Min, Daihu Ying, Zhang Guang, Zhaoyi Southwest Jiaotong University State Key Laboratory of Traction Power Chengdu Sichuan610031 China College of Advanced Manufacturing Technology Guangdong Institute of Mechanical and Electrical Technology Guangzhou510550 China

ISBN: (纸本)9798350323818

With the implementation of the development program of our country's transportation country, the traffic construction has welcomed a booming development, the passenger transportation and freight transportation increasing year by year, but it brings more and more high requirements on the quality of the steel rail, the steel rail due to the long-term service and all kinds of defects appear, especially with the rolling contact fatigue cracks. A method and system of rail surface crack identification and detection based on deep learning is proposed. The crack features can be accurately extracted from the complex image of rail surface for real-time crack identification and detection. The crack width can be identified as 0.1mm, and the recognition speed can reach 1-2ms. The rapid identification and detection of rail surface cracks are completed. © 2023 IEEE.

关键词： Freight transportation

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Dental Periapical Lesion Detection with Retinex image processing and an Efficient deep learning Model

Dental Periapical Lesion Detection with Retinex Image Proces...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： C.A. Aslin Christo Julia Punitha Malar Dhas Deva Priya Computer Science and Engineering Karunya University Coimbatore India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Dental periapical lesions help doctors understand your oral health needs and must be found accurately to deliver proper treatment. The research introduces a new way to find dental periapical lesions by combining Retinex image processing with YOLOv8 object detection. The Retinex method lets the model detect periapical lesions more accurately because it enhances image details while removing picture noise. Test outcomes show enhanced lesion detection that performs well with precise accuracy in tough image scenarios. The research demonstrates how advanced image processing helps medical imaging deep learning systems work better. The Objective of this work is to improve visibility of periapical lesions by Retinex based enhancement and fast detection through YOLOv8.

关键词： deep learning Radiography Visualization Object detection real-time systems Skin Dentistry Lesions X-ray imaging Biomedical imaging

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Underwater image processing and Target Detection Algorithm Based on deep learning

Underwater Image Processing and Target Detection Algorithm B...

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作者： Zheng, Yangning Yang, Yang School of Medicine Nanchang Institute of Technology Jiangxi Nanchang China School of Electronics and Information Nanchang Institute of Technology Jiangxi Nanchang China

With the growth of the earth’s population, people’s demand for space and resources continues to grow, making marine environmental research and exploration a new field that human development needs. The composition of the marine environment is unique to humans, and the application of intelligent underwater vehicle equipment with the ability to autonomously detect and recognize the environment can expand the scope of human research in the ocean. This article focuses on the research of underwater image processing and target detection algorithms based onDL, and understands the relevant theories of underwater image processing and target detection based on the literature, and then the underwater image processing and target detection algorithms based on DL In the design, in order to improve the accuracy of target detection, the DL model is optimized, and the optimized model is tested. The test result shows that if the Atrous convolution operation is added, the calculation amount of the network model will increase, resulting in *** speed drops to 50FPS. But after adding Atrous processing, the detection accuracy will be improved accordingly. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： deep learning

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License Plate reader with PUC Details using image processing and deep learning 2

License Plate reader with PUC Details using Image Processing...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Patil, R.S. Deshpande, Sakshi Khan, Hassan Mhatre, Prajakta Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Mumbai India

ISBN: (纸本)9798350348002

As each vehicle is uniquely acknowledged by its license plate, the Transport System places a high priority on finding and recognizing of license plates. The news is constantly reporting on accidents and missing cars. Authorities must acknowledge all of these unlawful acts. As a result, research into the identification and recognition of vehicle number plates is ongoing. However, identifying a vehicle's number plate has always been difficult for a number of reasons, such as brightness changes, shadows cast by moving vehicles, erratic license plate character types, different plate styles, and color effects caused by the surroundings. In this system, Number plate of vehicle is detected from a live video or an image. There is image preprocessing and segmentation done on the live video or number plate image. deep learning model methods are used, the characters from it are separated and then each character gets recognized. This helps to collect the vehicle overall project then the capabilities of different techniques into one integrated automatic system are summarized. This kind of systems can be implemented on the roadside and makes a real time comparison between passing car and list of stolen cars. This detected license plate number could also be used in car parking systems. PUC which stands for Pollution Under Control, where emission levels of vehicles and the regular renovation of the PUC certificate is done or not is verified and the details are shown. This will help in keeping an overall check on vehicles and the task which most of the places do manually to check the PUC certificate for checking status, can be verified quickly and the fine can be implemented as per so. © 2023 IEEE.

关键词： Optical character recognition

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Experimental Analysis of Malarial Parasites Infection by using deep learning based on Medical image processing Logic

Experimental Analysis of Malarial Parasites Infection by usi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： N Meenakshisundaram Sajiv G Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Malaria remains a significant global health challenge, particularly in resource-limited regions, necessitating accurate and rapid diagnostic tools. This study introduces deepMalariaNet, a deep learning model developed for detecting and classifying malarial parasites using the “Malaria Parasite image - Malaria Species” dataset from Kaggle. The model employs Residual Attention Mechanisms and Parallel Convolutional Stacks (PCS) to improve diagnostic accuracy by focusing on critical image regions and capturing multi-scale features. Experimental results demonstrate that deepMalariaNet achieves 98.5% accuracy for binary classification (infected vs. non-infected) and 95.2% for multiclass classification (species identification). The model's robustness is validated through 10-fold cross-validation and ablation studies, and it outperforms state-of-the-art models such as ResNet-50 and DenseNet-121 in both accuracy and inference time. deepMalariaNet shows significant promise for real-time malaria detection in clinical settings, contributing to early and accurate diagnosis, which is crucial for effective malaria control and treatment.

关键词： deep learning Accuracy Attention mechanisms Malaria Parasitic diseases Sensitivity and specificity Metadata Robustness real-time systems Integrated circuit modeling

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deep learning-based image processing framework for efficient surface litter detection in Computer Vision applications

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JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2025年第2期18卷

作者： Shad, Ifza Zhang, Zuping Asim, Muhammad Al-Habib, Mohammed Chelloug, Samia Allaoua Abd El-Latif, Ahmed Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci Lab Riyadh 11586 Saudi Arabia Prince Sultan Univ Ctr Excellence Quantum & Intelligent Comp Riyadh 11586 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Menoufia Univ Fac Sci Dept Math & Comp Sci Shibin Al Kawm 32511 Egypt

Computer vision plays a vital role in automating environmental analysis by enabling real-time object detection and classification in diverse conditions. Litter pollution poses significant health and environmental risks due to inefficient disposal, and manual oversight is labor intensive. Effective litter detection is crucial for large-scale environmental monitoring. However, existing models face challenges such as the complexity of detecting shadowy objects in varying lighting conditions (e.g., during rain or under sun rays), difficulty in recognizing small objects, low accuracy, and poor real-time performance. Existing two-stage detectors, such as Faster R-CNN, also struggle with these issues. This paper introduces an automated deep learning-based image processing approach for accurate litter detection across different locations, using an enhanced version of YOLOv9s called LD-YOLOv9s. Key improvements in this novel approach include replacing convolutional layers with DynConvLayer in the backbone, integrating an SDConv-ADown module to substitute down-sampling layers in the neck, and using MPD-IoU instead of CIoU. These modifications reduce the chances of overlooking small objects, such as caps or lids, which had the least class meaning in the dataset, achieving a mAP of 78.3% with an inference time of 6.7ms. A significant contribution of this work is the LD-2024 dataset, curated from indoor and outdoor environments with manually annotated images. Performance comparisons were made with several YOLO versions (YOLOv3, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9 variants) and traditional object detectors (Faster R-CNN, Center Net, Retina Net, Cascade R-CNN). Ablation studies validate the effectiveness of LD-YOLOv9s, which outperforms conventional methods, achieving a 6.3% improvement in mean average precision (mAP) over YOLOv9s on the LD-2024 dataset.

关键词： deep learning Computer vision image processing Litter Detection YOLO DynConvLayer SDConv MPD-IoU LD-YOLOv9s Object Detection

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Lane detection using the concept of deep learning and Digital image processing

Lane detection using the concept of Deep learning and Digita...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Shashi Kant Ojha Abhishek Kumar Sandeep Kumar CSE Department Sharda University Greater Noida

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Lane detection technology plays a pivotal role in enabling autonomous navigation in vehicles. However, existing systems primarily cater to well-structured roads with clear lane markings, rendering them ineffective in scenarios where markings are unclear or absent. This study critically evaluates an existing approach for detecting lanes on unmarked roads, followed by the proposal of an enhanced methodology. Both approaches leverage digital image processing techniques and rely solely on vision or camera data. The primary objective is to derive real-time curvature values to facilitate driver-assistance systems in making necessary turns and preventing vehicles from veering off-road.

关键词： deep learning Computers Lane detection Navigation Roads Digital images Rendering (computer graphics) Cameras real-time systems Proposals

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