Fast helmet-wearing-condition detection based on improved YOLOv2

Ming FANG; Teng-teng SUN; Zhen SHAO

doi:10.3788/OPE.20192705.1196

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Fast helmet-wearing-condition detection based on improved YOLOv2

Information Sciences | 更新时间：2020-08-13

- Fast helmet-wearing-condition detection based on improved YOLOv2
- Optics and Precision Engineering Vol. 27, Issue 5, Pages: 1196-1205(2019)
- 作者机构：
  
  长春理工大学计算机科学技术学院，吉林长春 130022
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192705.1196
  CLC： TP391.4
- Received：16 October 2018，
  
  Accepted：14 December 2018，
  
  Published：15 May 2019
- 稿件说明：
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Ming FANG, Teng-teng SUN, Zhen SHAO. Fast helmet-wearing-condition detection based on improved YOLOv2[J]. Optics and precision engineering, 2019, 27(5): 1196-1205.
DOI：

Ming FANG, Teng-teng SUN, Zhen SHAO. Fast helmet-wearing-condition detection based on improved YOLOv2[J]. Optics and precision engineering, 2019, 27(5): 1196-1205. DOI： 10.3788/OPE.20192705.1196.

摘要

施工现场光照多变、背景复杂、施工人员形态多样，给安全帽佩戴情况检测带来很大的困难。针对传统检测方法准确率低、鲁棒性差的问题，本文提出了一种基于深度学习的安全帽佩戴情况检测方法。该方法以YOLOv2目标检测方法为基础，对其网络结构进行了改进。首先借鉴了密集连接网络思想，在原网络中加入了密集块，实现了多层特征的融合以及浅层低语义信息与深层高语义信息的兼顾，提高了网络对于小目标检测的敏感性；然后，利用MobileNet中的轻量化网络结构对网络进行压缩，使模型的大小缩减为原来的十分之一，增加了模型的可用性。采用自制的HelmetWear数据集对改进后的网络模型进行训练和测试，并将该模型与原YOLOv2和最新的YOLOv3进行了对比，结果显示：该模型的检测准确率为87.42%，稍逊色于YOLOv3，但是其检测速度提升显著，比YOLOv2和YOLOv3分别提高了37%和215%，可达148 frame/s。实验表明，改进后的网络模型能在保证检测准确率的同时，有效减小参数量，显著提升检测速度。

Abstract

Construction sites comprise changeable illumination

complicated backgrounds

and various types of construction personnel

which makes the detection of helmet wearing challenging. To address the problems of low accuracy and poor robustness of traditional detection methods

this paper proposed a method of helmet wearing detection based on deep learning. The proposed method was based on the YOLOv2 target detection

and its network structure was improved. First

utilizing the notion of densely connected networks

dense blocks are added to the original network

which aids in the realization of the fusion of multi-layer features and the combination of shallow low semantic information and deep high semantic information

thereby improving the network sensitivity to enable the detection of small targets. Subsequently

the lightweight network structure in MobileNet was used for network compression

thus reducing the size of the model to one tenth of its original and also increasing model availability. The improved network model was trained and tested on the self-made HelmetWear dataset and compared with the original as well as the latest YOLOv3. The obtained results show that the detection accuracy of the model is 87.42%

which is slightly lower than that of YOLOv3

but its detection speed is significantly improved

i.e.

37% and 215% higher than that of YOLOv2 and YOLOv3

respectively

while reaching 148 frames/s. Experiments confirm that the proposed model can effectively reduce parameter quantity and significantly enhance detection speed while ensuring detection accuracy.

关键词

Keywords

references

刘晓慧, 叶西宁.肤色检测和Hu矩在安全帽识别中的应用[J].华东理工大学学报(自然科学版), 2014, 40(3):365-370.

LIU X H, YE X N. Skin color detection and hu moments in helmet recognition research[J]. Journal of East China University of Science and Technology(Natural Science Edition), 2014, 40(3):365-370. (in Chinese)

PARK M W, ELSAFTY N, ZHU Z. Hardhat-wearing detection for enhancing on-site safety of construction workers[J]. Journal of Construction Engineering & Management, 2015, 141(9).

周艳青, 薛河儒, 姜新华, 等.基于LBP统计特征的低分辨率安全帽识别[J].计算机系统应用, 2015, 24(7):211-215.

ZHOU Y Q, XUE H R, JIANG X H, et al.. Low-resolution safety helmet image recognition combining local binary pattern with statistical features[J]. Computer Systems & Applications, 2015, 24(7):211-215. (in Chinese)

潘蓉, 孙伟.基于预分割和回归的深度学习目标检测[J].光学精密工程, 2017, 25(10s):221-227.

PAN R, SUN W. Deep learning target detection based on pre-segmentation and regression[J]. Opt. Precision Eng., 2017, 25(10s):221-227. (in Chinese)

GIRSHICK R, DONAHUE J, DARRELL T, et al.. Region-based convolutional networks for accurate object detection and segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 38(1):142-158.

GIRSHICK R. Fast R-CNN[C]. IEEE International Conference on Computer Vision. 2015: 1440-1448.

REN S, HE K, GIRSHICK R, et al .. Faster R-CNN: towards real-time object detection with region proposal networks[C]. International Conference on Neural Information Processing Systems. MIT Press , 2015: 91-99.

HE K, ZHANG X, REN S, et al .. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 37(9):1904-16.

DAI J, LI Y, HE K, et al.. R-FCN: Object detection via region-based fully convolutional networks[J]. arXiv : 1605.06409v2, 2016. http://arxiv.org/abs/1605.06409

REDMON J, DIVVALA S, GIRSHICK R, et al .. You only look once: unified, real-time object detection[C]. Computer Vision and Pattern Recognition. IEEE , 2016: 779-788.

REDMON J, FARHADI A. YOLO9000: Better, Faster, Stronger[C]. IEEE Conference on Computer Vision and Pattern Recognition. IEEE , 2017: 6517-6525.

REDMON J, FARHADI A. YOLOv3: An Incremental Improvement[J]. arXiv : 1804.02767v1, 2018. http://arxiv.org/abs/1804.02767

LIU W, ANGUELOV D, ERHAN D, et al .. SSD: Single Shot MultiBox Detector[C]. European Conference on Computer Vision. Springer, Cham , 2016: 21-37.

孙腾腾. HelmetWear Dataset[EB/OL](2018-10-03)[2018-12-15] . https://pan.baidu.com/s/1PYGcfD9nA5pRANzL4UJJFg https://pan.baidu.com/s/1PYGcfD9nA5pRANzL4UJJFg

HUANG G, LIU Z, WEINBERGER K Q, et al .. Densely connected convolutional networks[C]. IEEE conference on Computer Vision and Pattern Recognition , 2016: 4700-4708.

吴天舒, 张志佳, 刘云鹏, 等.基于改进SSD的轻量化小目标检测算法[J].红外与激光工程, 2018, 47(7):47-53.

WU T SH, ZHANG ZH J, LIU Y P, et al.. A lightweight small object detection algorithm based on improved SSD[J]. Infrared and Laser Engineering, 2018, 47(7):47-53. (in Chinese)

HOWARD A G, ZHU M, CHEN B, et al.. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[J]. arXiv : 1704.04861v1, 2017. http://www.arxiv.org/abs/1704.04861

SANDLER M, HOWARD A, ZHU M, et al.. MobileNetV2: Inverted residuals and linear bottlenecks[J]. arXiv : 1801.04381v3, 2018. http://arxiv.org/abs/1801.04381

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