Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations
OOO «Videointellect», Skolkovo Innovation Centre,
42 Bolshoy boulevard,
2 The Laboratory of Information Technologies, JINR, 6 Joliot-Curie, 141980 Dubna, Moscow Region, Russian Federation
3 Institute of Experimental Physics, Slovak Academy of Sciences Watsonova 47, 04001 Košice, Slovak Republic
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Published online: 20 January 2020
The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.