Improved Algorithm of FCOS for Complex Scene Mask Wear Detection

doi:10.3778/j.issn.1002-8331.2203-0048

Abstract

Abstract: 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.

Key words: multi-scale, deformable convolution, label assignment strategy, Generalized Focal Loss

摘要： 针对在复杂场景口罩佩戴检测中存在的多尺度、多角度和遮挡等问题，提出一种基于改进FCOS的复杂场景口罩佩戴检测算法。在算法的骨干网络中引入Res2Net的分组残差连接结构，提高网络对不同尺度口罩的特征提取能力，并在其中集成可变形卷积，拓展其对未知形状物体的建模能力；设计一种集成注意力机制的特征金字塔，为不同的特征通道赋予不同的权重，抑制无用的特征信息；根据目标口罩的相关统计特征自动地划分正负样本，提高不同尺度口罩的样本质量，并引入Generalized Focal Loss联合训练样本的分类分数和定位质量分数，提升算法性能。实验结果表明，在复杂场景下的口罩佩戴检测中，该改进算法的mAP相比于原始FCOS提高6.7个百分点，同时与一些主流的目标检测算法相比，该改进算法也具有更好的效果和鲁棒性。

关键词: 多尺度, 可变形卷积, 标签分配策略, Generalized Focal Loss

WEI Chiyu, LIU Rong, LIU Ming, ZHANG Xinyue. Improved Algorithm of FCOS for Complex Scene Mask Wear Detection[J]. Computer Engineering and Applications, 2023, 59(11): 188-194.

魏驰宇, 刘蓉, 刘明, 张心月. 改进FCOS的复杂场景口罩佩戴检测算法[J]. 计算机工程与应用, 2023, 59(11): 188-194.

References

[1] 王迪聪，白晨帅，邬开俊.基于深度学习的视频目标检测综述[J].计算机科学与探索，2021，15（9）：1563-1577.
WANG D C，BAI C S，WU K J，et al.Survey of video object detection based on deep learning[J].Journal of Frontiers of Computer Science and Technology，2021，15（9）：1563-1577.
[2] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39（6）：1137-1149.
[3] TIAN Z，SHEN C，CHEN H，et al.FCOS：fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：9627-9636.
[4] LIN T，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：2980-2988.
[5] CHEN Q，WANG Y，YANG T，et al.You only look one-level feature[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：13039-13048.
[6] 牛作东，覃涛，李捍东，等.改进RetinaFace的自然场景口罩佩戴检测算法[J].计算机工程与应用，2020，56（12）：1-7.
NIU Z D，QIN T，LI H D，et al.Improved algorithm of RetinaFace for natural scene mask wear detection[J].Computer Engineering and Applications，2020，56（12）：1-7.
[7] DENG J，GUO J，VERVERAS E，et al.RetinaFace：single-shot multi-level face localisation in the wild[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：5203-5212.
[8] 叶子勋，张红英.YOLOv4口罩检测算法的轻量化改进[J].计算机工程与应用，2021，57（17）：157-168.
YE Z X，ZHANG H Y.Lightweight improvement of YOLOv4 mask detection algorithm[J].Computer Engineering and Applications，2021，57（17）：157-168.
[9] WANG C，BOCHKOVSKIY A，LIAO H M.Scaled-YOLOv4：scaling cross stage partial network[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：13029-13038.
[10] HOWARD A，SANDLER M，CHU G，et al.Searching for MobileNetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：1314-1324.
[11] 毛晓波，徐向阳，李楠，等.基于改进SSD和Jetson Nano的口罩佩戴检测门禁系统[J].郑州大学学报（工学版），2021，42（6）：85-92.
MAO X B，XU X Y，LI N，et al.A mask wear detection access control system based on improved SSD and Jetson Nano[J].Journal of Zhengzhou University（Engineering Science），2021，42（6）：85-92.
[12] LIU W，ANGUELOV D，ERHAN D，et al.SSD：single shot multibox detector[C]//European Conference on Computer Vision，2016：21-37.
[13] GAO S，CHENG M，ZHAO K，et al.Res2Net：a new multi-scale backbone architecture[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2021，43：652-662.
[14] ZHU X，HU H，LIN S，et al.Deformable ConvNets v2：more deformable，better results[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：9308-9316.
[15] HU J，SHEN L，ALBANIE S，et al.Squeeze-and-excitation networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2020，42（8）：2011-2023.
[16] ZHANG S，CHI C，YAO Y，et al.Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：9759-9768.
[17] LI X，WANG W，WU L，et al.Generalized focal loss：learning qualified and distributed bounding boxes for dense object detection[C]//Advances in Neural Information Processing Systems，2020：21002-21012.
[18] LIN T，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2117-2125.
[19] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[20] YANG Z，LIU S，HU H，et al.RepPoints：point set representation for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：9657-9666.
[21] FENG C，ZHONG Y，GAO Y，et al.TOOD：task-aligned one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2021：3510-3519.
[22] MICHAELIS C，MITZKUS B，GEIRHOS R，et al.Benchmarking robustness in object detection：autonomous driving when winter is coming[J].arXiv：1907. 07484，2019.