Segmenting Image and Removing Obtrusive Elements

This Model offers a novel method for processing images that highlights the main subject and removes unnecessary features using YOLO, Darknet, picture segmentation, and Mask R-CNN." The approach accomplishes accurate object detection by using procedures like segmentation using Mask R-CNN, YOLO a...

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Bibliographic Details
Published in2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT) Vol. 1; pp. 1 - 7
Main Authors Singh, Anirudh, Gupta, Vishank, Gupta, Yash, Kamboj, Dikshant, Singh, Ashwini Kumar, Malik, Preeti
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.08.2024
Subjects
Accuracy
Autonomous vehicles
Background noise
CNN
Computational modeling
Dark Web
Darknet
Dataset
Image retrieval
Image segmentation
Mask R-CNN
Object recognition
Surveillance
YOLO
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DOI10.1109/ICEECT61758.2024.10739137

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Summary:This Model offers a novel method for processing images that highlights the main subject and removes unnecessary features using YOLO, Darknet, picture segmentation, and Mask R-CNN." The approach accomplishes accurate object detection by using procedures like segmentation using Mask R-CNN, YOLO and Darknet object identification, and backdrop blackening. It is highly skilled at identifying primary items and has been trained on a large photo dataset. Its uses include autonomous driving, object detection, and image retrieval. By drastically lowering background noise, the detection component improves the accuracy of photo object detection. Essentially, this technique is critical to improving object detection accuracy through the reduction of background noise, which is an essential component of image processing. Finding and keeping important components while eliminating unnecessary ones from test cases or camera feeds is the main goal of the Model.
DOI:10.1109/ICEECT61758.2024.10739137