Detection of Pedestrians and Helmets in Large Construction Site
Keywords:pedestrians and helmets’ detection, large construction site, Faster R-CNN, Parallel Residual Block
It is necessary for workers to wear helmets when working in large construction sites. The traditional way to supervise the workers whether wearing helmets or not for safety is artificial, which have brought out many problems such as many blind spots, labor and time costing. Since a large number of surveillance cameras are currently installed in these construction sites, the surveillance video can be developed in taking the place of human supervision in an intelligent way. This paper designs a pedestrian and helmet detection network based on Faster R-CNN. In feature extraction, we have chosen the Residual Network (Resnet) combined with the Feature Pyramid Network (FPN) because the objects have small size, low resolution and less semantic information in whole scenes. We have also designed a parallel residual Block (PRB) combined with the Receptive Field Block (RFB) to strength feature extraction. The feature maps obtained from different convolution layers have been fused twice. And we have studied two fusion methods. Experiment results from our own dataset show that our proposed detection network improves the mAP by 8.74% and 2.3% respectively compared with Yolov3 and Faster R-CNN, at the cost of 0.3 FPS slower than Faster R-CNN.
. V Zeiler, Matthew D, and R. Fergus. "Visualizing and Understanding Convolutional Networks."European Conference on Computer Vision Springer (2014).
. Girshick, Ross. "Fast R-CNN." Computer ence (2015).
. Ren, Shaoqing, et al. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." IEEE Transactions on Pattern Analysis & Machine Intelligence 39.6(2017):1137-1149.
. Zhang, Hanwang, et al. "PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN." 2017 IEEE International Conference on Computer Vision (ICCV) (2017).
. Liu, Wei, et al. "SSD: Single Shot MultiBox Detector." European Conference on Computer Vision Springer International Publishing (2016).
. Redmon, Joseph, et al. "You Only Look Once: Unified, Real-Time Object Detection." Computer Vision & Pattern Recognition IEEE (2016).
. Lin, Tsung Yi, et al. "Focal Loss for Dense Object Detection." IEEE Transactions on Pattern Analysis & Machine Intelligence PP.99(2017):2999-3007.
. Gan, Guolong, and J. Cheng. "Pedestrian Detection Based on HOG-LBP Feature." Seventh International Conference on Computational Intelligence & Security IEEE (2012).
. Guo, Lie, et al. "Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine." Expert Systems with Applications 39.4(2012):4274-4286.
. Xiao-Hui, Liu, and Y. E. Xi-Ning. "Skin Color Detection and Hu Moments in Helmet Recognition Research." Journal of East China University of ence and Technology (2014).
. Zhang, Geng, et al. "The Method for Recognizing Recognition Helmet Based On Color and Shape." International Conference on Machinery (2017).
. Ouyang, Wanli, and X. Wang. "Joint Deep Learning for Pedestrian Detection." IEEE International Conference on Computer Vision IEEE (2014).
. Rohith, C A, et al. "An Efficient Helmet Detection for MVD using Deep learning." 2019 3rd International Conference on Trends in Electronics and Informatics (2019).
. Kaiming, He , et al. "Mask R-CNN." IEEE Transactions on Pattern Analysis & Machine Intelligence PP(2017):1-1.
. He, Kaiming, et al. "Deep Residual Learning for Image Recognition." IEEE Conference on Computer Vision & Pattern Recognition IEEE Computer Society (2016).
. Lin, Tsung Yi, et al. "Feature Pyramid Networks for Object Detection." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Computer Society (2017).
. Liu Songtao, Huang Di, Wang Yunhong. "Receptive Field Block Net for Accurate and Fast Object Detection." 2017 IEEE Conference on Computer Vision and Pattern Recognition (2017).
. Zhang, Xiaohu, Y. Zou, and W. Shi. "Dilated convolution neural network with LeakyReLU for environmental sound classification." 2017 22nd International Conference on Digital Signal Processing (2017).
How to Cite
Authors who submit papers with this journal agree to the following terms.