Real-Time Construction Worker Detection Method for Edge Device

doi:10.3778/j.issn.1002-8331.2105-0362

Abstract

Abstract:

Construction worker detection has an important application value in construction management. The complex images backgrounds and diverse perspectives at construction sites make the task of construction worker detection difficult, while most of the construction sites have poor infrastructure facilities and poor network conditions, making them unsuitable for model deployment on large GPU workstations. To address the above issues, it proposes a real-time worker detection algorithm for construction scenes based on YOLOv3 detection network, adding a feature pyramid pooling module, increasing multi-scale feature fusion and improving candidate frames to improve detection accuracy, and using a channel pruning algorithm to lighten the detection network to adapt to the computing power of edge-end devices. This method achieves an average accuracy of 88.23% on the homemade construction worker dataset, which is 4.89 percentage points higher than the YOLOv3 detection method, and compresses the model size to 1/13 of the original one, doubles the detection speed. It achieves a detection speed of 69.08 frame/s on the embedded end device, meeting the requirements for real-time edge-end detection at construction sites.

Key words: pedestrian detection, YOLOv3, edge computing, channel pruning

摘要：

施工人员检测在施工管理工作中有重要的应用价值。施工现场图像背景复杂且视角多样，给施工人员检测任务带来难度，同时施工现场大多基础配套设施不完善，并且网络条件较差，不适合在大型GPU工作站上进行模型部署。针对以上问题，以YOLOv3检测网络为基础，加入特征金字塔池化模块，增加多尺度特征融合并改进候选框，提升检测精度，同时采用通道剪枝算法对检测网络进行轻量化处理以适应边缘端设备算力，提出一种面向边缘端的施工人员实时检测方法。该方法在自制的施工人员数据集上平均准确率可达到88.23%，较YOLOv3检测方法提升4.89个百分点，且将模型大小压缩至原来的1/13，检测速度提升一倍，在嵌入式设备Jetson Xavier NX上检测速度可达到69.08帧/s，满足在施工现场进行实时边缘端检测的要求。

关键词: 人员检测, YOLOv3, 边缘计算, 通道剪枝

XU Xiaodong, WANG Junjie. Real-Time Construction Worker Detection Method for Edge Device[J]. Computer Engineering and Applications, 2021, 57(23): 280-286.

徐晓东，王俊杰. 面向边缘端的施工人员实时检测方法[J]. 计算机工程与应用, 2021, 57(23): 280-286.

References

[1] FANG W，DING L，ZHONG B，et al.Automated detection of workers and heavy equipment on construction sites：a convolutional neural network approach[J].Advanced Engineering Informatics，2018，37：139-149.
[2] ANGAH O，CHEN A Y.Tracking multiple construction workers through deep learning and the gradient based method with re-matching based on multi-object tracking accuracy[J].Automation in Construction，2020，119：103308.
[3] GIRSHICK R，DONAHUE J，DARRELL，et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2014：580-587.
[4] GIRSHICK R.Fast R-CNN[C]//IEEE International Conference on Computer Vision，2015：1440-1448.
[5] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Neural Information Processing Systems，2015：91-99.
[6] LIU W，ANGUELOV D，ERHAN D，et al.Single shot multibox detector[C]//European Conference on Computer Vision，2016：21-37.
[7] REDMON J，DIVVALA S，GIRSHICK R，et al.You only look once：unified，real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition，2016：779-788.
[8] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition，2017：7263-7271.
[9] LIU S，HUANG D.Receptive field block net for accurate and fast object detection[C]//European Conference on Computer Vision.Cham：Springer，2018：385-400.
[10] 李昕昕，杨林.面向复杂道路场景小尺度行人的实时检测算法[J].计算机工程与应用，2020，56（22）：124-131.
LI Xinxin，YANG Lin.Real-time detection algorithm for small-scale pedestrians in complex road scenes[J].Computer Engineering and Applications，2020，56（22）：124-131.
[11] 魏润辰，何宁，尹晓杰.YOLO-Person：道路区域行人检测[J].计算机工程与应用，2020，56（19）：197-204.
WEI Runchen，HE Ning，YIN Xiaojie.YOLO-Person：pedestrian detection in road areas[J].Computer Engineering and Applications，2020，56（19）：197-204.
[12] LIU S，HUANG D.Receptive field block net for accurate and fast object detection[C]//European Conference on Computer Vision.Cham：Springer，2018：385-400.
[13] 王琳，卫晨，李伟山，等.结合金字塔池化模块的YOLOv2的井下行人检测[J].计算机工程与应用，2019，55（3）：133-139.
WANG Lin，WEI Chen，LI Weishan，et al.Pedestrian detection based on YOLOv2 with pyramid pooling module in underground coal mine[J].Computer Engineering and Applications，2019，55（3）：133-139.
[14] 李伟山，卫晨，王琳.改进的Faster RCNN煤矿井下行人检测算法[J].计算机工程与应用，2019，55（4）：200-207.
LI Weishan，WEI Chen，WANG Lin.Improved Faster RCNN approach for pedestrian detection in underground coal mine[J].Computer Engineering and Applications，2019，55（4）：200-207.
[15] HE K，ZHANG X，REN S，et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37（9）：1904-1916.
[16] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[17] ZHANG P，ZHONG Y，LI X.SlimYOLOv3：narrower，faster and better for real-time UAV applications[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2019.
[18] ZHUANG L，LI J，SHEN Z，et al.Learning efficient convolutional networks through network slimming[C]//2017 IEEE International Conference on Computer Vision（ICCV），2017.
[19] LIN T Y，MAIRE M，BELONGIE S，et al.Microsoft COCO：common objects in context[C]//European Conference on Computer Vision，2014.
[20] EVERINGHAM M，VAN GOOL L，WILLIAMS C K I，et al.The pascal visual object classes（VOC） challenge[J].International Journal of Computer Vision，2010，88（2）：303-338.
[21] AN X，ZHOU L，LIU Z，et al.Dataset and benchmark for detecting moving objects in construction sites[J].Automation in Construction，2021，122：103482.