An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
- Title
- An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
- Creator
- Hussain J.; Prathap B.R.; Sharma A.
- Description
- As object detection has gained popularity in recent years, there are many object detection algorithms available in today's world. Yet the algorithm with better accuracy and better speed is considered vital for critical applications. Therefore, in this article, the use of the YOLOV4 object detection algorithm is combined with improved and efficient inference methods. The YOLOV4 state-of-the-art algorithm is 12% faster compared to its previous version, YOLOV3, and twice as faster compared to the EfficientDet algorithm in the Tesla V100 GPU. However, the algorithm has lacked performance on an average machine and on single-board machines like Jetson Nano and Jetson TX2. In this research, we examine the performance of inferencing in several frameworks and propose a framework that effectively uses hardware to optimize the network while consuming less than 30% of the hardware of other frameworks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-462, pp. 305-316.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Object detection; Optimized inferencing; YOLOV4
- Coverage
- Hussain J., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Prathap B.R., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Sharma A., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981192210-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Hussain J.; Prathap B.R.; Sharma A., “An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20261.