| Abstrakt: |
Aiming at the problems of multi-scale, multi angle and occlusion in mask wearing detection in complex scenes, a mask wearing detection algorithm in complex scenes based on improved FCOS is proposed in this paper. Firstly, in order to improve the feature extraction ability of the network for masks with different scales, the packet residual connection structure of Res2Net is introduced into the backbone network of the algorithm, and the deformable convolution is integrated to expand its modeling ability for objects with unknown shapes. Then, a feature pyramid integrating attention mechanism is designed to give different weights to feature channels and suppress useless feature information. Finally, according to the relevant statistical characteristics of the target mask, the positive and negative samples are automatically divided to improve the sample quality of masks with different scales, and Generalized Focal Loss is introduced to jointly train the classification score and positioning quality score of samples so as to improve the performance of the algorithm. The experimental results show that the mAP of improved algorithm in this paper improves 6.7 percentage points compared with the original FCOS in the detection of mask wearing in complex scenes. Meanwhile, compared with some mainstream target detection algorithms, the improved algorithm in this paper also has better effect and robustness. [ABSTRACT FROM AUTHOR] |
