License Plate Detection and Recognition using Convolutional Neural Network

Gadiyar, H. Manoj T.; Thyagaraju, G S; B, Sahana Kumari; Kruthi, G S; Lavanya, R.; B, Ayisha

doi:10.46610/jonscn.2021.v07i01.002

Cited by 2 publications

(2 citation statements)

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“…The different signal processing techniques like microwave imaging, ultrasound imaging, wavelet transform which shows the time frequency representation of a signal using small waveform called as wavelet and curve-let [?]. It helps the breast cancer detection with its low frequency imagery where its degree of localization varies with scale and produce images on different scale [5]. Deep learning is one kind of machine learning technique in which a computer generated model performs the task of classification directly learning from sound, visual data or texts.…”

Section: Introductionmentioning

confidence: 99%

An Introductory Implementation of Breast Cancer Detection from Mammograms and Pixel Intensity with Efficient-Net Other Neural Nets

Banerjee,

Kabir

2024

Preprint

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In the world of civilized medical scientific progression, cancer has become a very serious threat for the natural survival of human beings where breast cancer stays to be the second most dangerous type. Mostly women are embracing very pathetic death because of the delayed detection of the cancer cell in the certain period of their life. Machine Learning mechanism can definitely help at the stage of medical imaging which can escalate the diagnosis of the cancer cells at a very early age of its biological formation and development. We focused upon the deep learning approach to classify the normal and abnormal breast according to the medical imaging from the MIAS dataset of Mammograms and Pixel Intensity. The Convolution Neural Network (CNN) alongside ResNet, AmoebaNet and EfficientNet have been used for the detection with 330 mammograms in which 194 images are normal and 136 are having the identification of abnormal breasts. The accuracy of the entire experimental results was carrying the torch of potential legacy of deep learning in the medical imaging arena. The research is ongoing for the further development and optimization of CNN, AmoebaNet-C and EfficientNet architecture for the Pixel Intensity with higher accuracy, proper segmentation and masking. Source code of this research is available here: https://github.com/ac005sheekar/Breast-CancerDetection-with-Pixel-Intensity/

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Section: Introductionmentioning

confidence: 99%

An Introductory Implementation of Breast Cancer Detection from Mammograms and Pixel Intensity with Efficient-Net Other Neural Nets

Banerjee,

Kabir

2024

Preprint

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“…India, the 2nd largest agricultural producer in the world, produces 330 million kilograms of arecanuts each year. Kerala and Karnataka, two states in India, cultivate 72% of India's arecanuts in a total area of 264,000 hectares of farming land (Anilkumar, M. G., 2021). However, maintaining and inspecting the large quantity of arecanuts is an enormous task for farmers like my grandparents who have to climb palm-like trees, around 10 to 20 meters tall, in order to examine the growth of arecanuts which are the size of golf balls.…”

Section: Introductionmentioning

confidence: 99%

AI/ML Drone-based Arecanut Monitoring System (ADAMS)

Prasad

Pidamale²

2022

J Stud Res

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The Indian economy relies heavily on agriculture with 72% of farmers in the states of Karnataka and Kerala producing 330 million kilograms of arecanuts. However, a rapid increase in young adults leaving the agriculture field brings hardships for older farmers like my grandparents since physical labor is increasingly expensive, dangerous, and scarce. The purpose of this research is to automate arecanut crop monitoring by providing a safe, affordable, user-friendly, and cutting-edge system that utilizes six major elements to achieve the goal: arecanut farm, drone, dataset collection, data annotations, AI/ML Mask R-CNN prediction model, and app. Research about various drones were conducted to ensure the drone abides by Indian UAV regulations. Since there were no pre-existing arecanut datasets, a DJI Mini 2 drone and Canon DSLR Camera were utilized to capture over 2000 arecanut images in India. Creating my own dataset from scratch was challenging, however it was a significant contribution in advancing computer vision. This dataset was stored on Amazon S3 and classified into ripe, unripe, and dry arecanuts. Each image was manually annotated using VGG Image Annotator and passed into a Convolutional Neural Network called Mask R-CNN to create a prediction model. This was trained on Amazon Web Services (AWS) for 100 epochs. Overall, epoch 85 gave accurate predictions. Additionally, instead of the DJI Mini 2 drone, the Skydio drone would be the best option for real world implementations. My research is a practical and innovative solution to alleviate farmers’ financial and physical hardships.

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License Plate Detection and Recognition using Convolutional Neural Network

Cited by 2 publications

References 0 publications

An Introductory Implementation of Breast Cancer Detection from Mammograms and Pixel Intensity with Efficient-Net Other Neural Nets

An Introductory Implementation of Breast Cancer Detection from Mammograms and Pixel Intensity with Efficient-Net Other Neural Nets

AI/ML Drone-based Arecanut Monitoring System (ADAMS)

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