Analysis of Object Detection Performance Based on Faster R-CNN. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Li, Wenze
• Is Part Of: Journal of physics. Volume 1827:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1827/1/012085
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The related regions with convolutional neural networks (R-CNN) models have been widely used in the field of object detection. Faster R-CNN significantly improves the overall performance by adding RPN, especially in terms of detection speed. However, the application of different pre-training models will result in a great difference in the performance of Faster R-CNN. This paper analyzed the performance of Faster R-CNN models based on different pre-training models and conducted a comprehensive evaluation of the performance of Faster R-CNN. The experimental results showed the accuracy and detection speed of R-CNN, fast R-CNN and faster R-CNN based on three different data sets. They can objectively and comprehensively evaluate the performance of R-CNN, fast R-CNN, and faster R-CNN.