Conference proceeding
Deep Learning in Object Detection: a Review
2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), pp 1-11
Aug 2020
Abstract
Object detection continues to play a significant part in computer vision theory, study and practical application. Conventional object detection algorithms were primarily derived from machine learning. This involved the design of features for describing the object's characteristics followed by an integration with classifiers. In recent years, the application of deep learning (DL), and more specifically Convolutional Neural Networks (CNN) have elicited a great advancement and promising progress, and has therefore, received much attention on the global stage of research about computer vision. This paper conducts a review about some of the most important and recent developments and contributions that have been made towards research in the use of deep learning in object detection. Moreover, as evidently demonstrated, the findings of numerous studies suggest that the application of deep learning in object detection much surpasses conventional approaches focused on handcrafted and learned features.
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63 citations in Scopus
Details
- Title
- Deep Learning in Object Detection: a Review
- Creators
- Katleho L Masita - University of JohannesburgAli N Hasan - Higher Colleges of TechnologyThokozani Shongwe - University of Johannesburg
- Publication Details
- 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), pp 1-11
- Publisher
- IEEE
- Number of pages
- 11
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Scopus ID
- 2-s2.0-85092042813
- Other Identifier
- 991022004617204721