IRNet: An Improved RetinaNet Model for Face Detection
Chenchen Jiang, Hongbing Ma, Liangliang Li · 2022 7th International Conference on Image, Vision and Computing (ICIVC) · 2022
The current face detection methods mainly focus on overlaying network layers to improve the detection accuracy. However, in practical applications, these huge models cannot achieve the real-time detection. In order to solve the aforementioned problems, an improved RetinaNet (IRNet) model for face detection is proposed. In the field of face detection, feature fusion module is a common method to solve multi-scale detection problems. In this paper, a new feature fusion module N-FPN is introduced. The N-FPN module has been proven to improve detection accuracy. We design experiments to select the appropriate weight decay parameters. The IRNet finally achieves 91.77%, 89.26%, and 76.59% detection results on Easy, Medium, and Hard datasets of Wider Face, respectively. In addition, the IRNet obtained better detection results than RetinaNet on the FDDB dataset.