An improved deep learning-based optimal object detection system from images
Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is op...
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| Published in | Multimedia tools and applications Vol. 83; no. 10; pp. 30045 - 30072 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1573-7721 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-023-16736-5 |
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| Abstract | Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss. |
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| AbstractList | Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss. |
| Author | de Albuquerque, Victor Hugo C. dos Santos Nascimento, Caio Yadav, Satya Prakash Rani, Preeti Jindal, Muskan Kumar, Manoj |
| Author_xml | – sequence: 1 givenname: Satya Prakash surname: Yadav fullname: Yadav, Satya Prakash organization: Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Graduate Program in Telecommunications Engineering. (PPGET), Federal Institute of Education, Science, and Technology of Ceará (IFCE) – sequence: 2 givenname: Muskan surname: Jindal fullname: Jindal, Muskan organization: Department of Computer Science and Engineering, Amity University – sequence: 3 givenname: Preeti surname: Rani fullname: Rani, Preeti organization: Department of Electronics & Communication Engineering, SRM Institute of Science and Technology – sequence: 4 givenname: Victor Hugo C. surname: de Albuquerque fullname: de Albuquerque, Victor Hugo C. organization: Department of Teleinformatics Engineering, Federal University of Ceará – sequence: 5 givenname: Caio surname: dos Santos Nascimento fullname: dos Santos Nascimento, Caio organization: Department of Teleinformatics Engineering, Federal University of Ceará – sequence: 6 givenname: Manoj orcidid: 0000-0001-5113-0639 surname: Kumar fullname: Kumar, Manoj email: wss.manojkumar@gmail.com organization: School of Computer Sceince, FEIS, University of Wollongong in Dubai, MEU Research Unit, Middle East University |
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| Keywords | Chess Piece Identification Object Detection Single Stage Detector (SSD) You Only Look Once (YOLO) Faster Region-Based Convolutional Neural Networks (R-CNN) |
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