Analysis of Object Detection Performance Based on Faster R-CNN

Wenze Li

2021Journal of Physics Conference Series70 citationsDOI Open Access PDF

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.

Topics & Concepts

Convolutional neural networkComputer scienceArtificial intelligencePattern recognition (psychology)Object detectionField (mathematics)MathematicsPure mathematicsAdvanced Neural Network ApplicationsImage and Object Detection TechniquesIndustrial Vision Systems and Defect Detection