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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Object Detection using deep learning |
| Authors Name: | T. Yeshwanth Reddy , P. SaiCharan Reddy , G. Tejeshwar Reddy , G. Arjun Reddy |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217810
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| Published Paper Id: | IJNRD2404239 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | One crucial computer vision problem is object detection, which is locating and identifying items in an image or video. Convolutional neural networks (CNNs), one type of deep learning approach, have produced state-of-the-art outcomes in object detection tasks. The goal of this research is to create a deep learning-based object detection model that can precisely locate and identify a number of things in intricate scenarios. The model is going to be trained on a dataset of photos that have bounding box annotations. Evaluations will be made of the effectiveness of several CNN designs, including ResNet and VGGNet, as well as training methodologies. We will use data augmentation methods like color jittering, flipping, and rotation to add more variation to the training set. The goal of the model's training process will be to maximize a loss function that accounts for both bounding box regression error and classification error. We will evaluate a number of optimization techniques, including Adam, RMSprop, and stochastic gradient descent. Model selection, hyperparameter tweaking, and other design decisions will be guided by the validation set performance. Using a test set, the final model will be quantitatively assessed in terms of mean average precision, which is a common item detection metric. Studies on ablation will examine the effects of various model elements and training regimens. An object detection system with high accuracy that can function in real-time and surpass 90% mAP is the aim. |
| Keywords: | Object Detection using deep learning |
| Cite Article: | "Object Detection using deep learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.c71-c74, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404239.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404239 Registration ID: 217810 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: c71-c74 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404239 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404239 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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