Papers by PONNEELA VIGNESH
International journal of novel research and development , 2023
This paper describes a local transport tracking system, which utilizes mobile devices, GPS receiv... more This paper describes a local transport tracking system, which utilizes mobile devices, GPS receivers and an online tracking application to provide real-time monitoring of public transportation vehicles. The system allows users to track the locations of buses, trains, and other modes of public transportation, as well as to view information on routes and arrival times. The system also provides a platform for communication between commuters and transit operators, allowing for improved customer service. The tracking system is designed to be affordable and easy to use, making it accessible for public transportation operators of any size. The system has been tested and proven to be reliable and effective providing significant improvements in the quality and efficiency of local transportation services.
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International journal of modern engineering research, 2024
This research focuses on the implementation of the YOLOv8 (You Only Look Once version
8) algorit... more This research focuses on the implementation of the YOLOv8 (You Only Look Once version
8) algorithm for real-time detection of fire and smoke in diverse environments. YOLOv8 is renowned for
its speed and accuracy in object detection, making it a suitable candidate for fire and smoke detection
applications. The proposed system involves training the YOLOv8 algorithm on a curated dataset
containing annotated images of fire and smoke scenarios. The training process aims to optimize the
model's ability to accurately identify and localize these critical elements in various environmental
conditions. The integration of the YOLOv8 algorithm into a real-time fire and smoke detection system will
be demonstrated, showcasing its potential for early detection and prompt response in
emergency situations.
Keywords: Fire and smoke detection, YOLO algorithm, YOLOv8, Ultralytics, Python.
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International Journal of Innovative Research in Science, Engineering and Technology, 2014
Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation... more Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation of real time algorithms suited to video image processing applications. The unique architecture of the FPGA has allowed the technology tope used in many applications encompassing all aspects of video image processing. Among those algorithms, linear filtering based on a 2D convolution, and non-linear 2D morphological filters, represent a basic set of image operations for a number of applications. In this work, an implementation of linear and morphological image filtering using a FPGA, Xilinx, Spartan 3E, with educational purposes, is presented. The system is connected to a USB port of a personal computer, which in that way form a powerful and low-cost design station. The FPGA-based system is accessed through a Mat lab graphical user interface, which handles the communication setup. A comparison between results obtained from MATLAB simulations and the described FPGA-based implementation is...
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Edge detection serves as a pre-processing step for many image processing algorithms such as image... more Edge detection serves as a pre-processing step for many image processing algorithms such as image enhancement, image segmentation, tracking and image/video coding. The edge detection is one of the key stages in image processing and object recognition. This paper present a Canny edge detection algorithm that results in significantly reduced memory requirements, decreased latency and increased throughput with no loss in edge detection performance. This edge detection algorithm is based on MATLAB simulation and FPGA implementation.
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Papers by PONNEELA VIGNESH
8) algorithm for real-time detection of fire and smoke in diverse environments. YOLOv8 is renowned for
its speed and accuracy in object detection, making it a suitable candidate for fire and smoke detection
applications. The proposed system involves training the YOLOv8 algorithm on a curated dataset
containing annotated images of fire and smoke scenarios. The training process aims to optimize the
model's ability to accurately identify and localize these critical elements in various environmental
conditions. The integration of the YOLOv8 algorithm into a real-time fire and smoke detection system will
be demonstrated, showcasing its potential for early detection and prompt response in
emergency situations.
Keywords: Fire and smoke detection, YOLO algorithm, YOLOv8, Ultralytics, Python.
8) algorithm for real-time detection of fire and smoke in diverse environments. YOLOv8 is renowned for
its speed and accuracy in object detection, making it a suitable candidate for fire and smoke detection
applications. The proposed system involves training the YOLOv8 algorithm on a curated dataset
containing annotated images of fire and smoke scenarios. The training process aims to optimize the
model's ability to accurately identify and localize these critical elements in various environmental
conditions. The integration of the YOLOv8 algorithm into a real-time fire and smoke detection system will
be demonstrated, showcasing its potential for early detection and prompt response in
emergency situations.
Keywords: Fire and smoke detection, YOLO algorithm, YOLOv8, Ultralytics, Python.