基于YOLOv3的建筑工地目标检测研究
Research on Construction Site Target Detection Based on YOLOv3
DOI: 10.12677/CSA.2021.1111283, PDF,    国家自然科学基金支持
作者: 王晓宇, 何 强, 王恒友, 刘屹伟：北京建筑大学理学院，北京；张长伦：北京建筑大学理学院，北京;北京建筑大学，北京未来城市设计高精尖创新中心，北京
关键词: 目标检测；建筑工地场景；YOLOv3；Noise2noise；Object Detection； Construction Site Scene； YOLOv3； Noise2noise
摘要: 随着智慧工地的产生和发展，建筑工地施工现场各类监测技术的要求日益提高，为了更好地监测施工现场各类行为是否符合规范需要提高目标检测算法的精确度。本文为了更准确地检测建筑工地场景下的真实图像，采用MOCS数据集验证目标检测效果。首先用无监督的深度学习去噪网络Noise2noise进行去噪，其次将去噪后的图像送入深度学习网络YOLOv3进行目标检测。经过去噪后的图像目标检测的效果有一定的提升。
Abstract: With the emergence and development of smart construction sites, the requirements of various monitoring technologies on construction sites are increasing day by day. In order to better monitor the compliance of various behaviors on construction sites, the accuracy of target detection algorithms needs to be improved. In order to more accurately detect the real image in the construction site scene, this paper uses MOCS data set to verify the target detection effect. Firstly, Noise2noise, an unsupervised deep learning denoising network, is used for denoising. Secondly, the denoised images are sent to YOLOv3, a deep learning network for target detection. After denoising, the effect of image target detection is improved to some extent.
文章引用：王晓宇, 张长伦, 何强, 王恒友, 刘屹伟. 基于YOLOv3的建筑工地目标检测研究[J]. 计算机科学与应用, 2021, 11(11): 2788-2794. https://doi.org/10.12677/CSA.2021.1111283

参考文献

[1] 李瑞平, 杜瑞. 智慧工地管理平台在建筑工程中的应用探究[J]. 智能建筑与智慧城市, 2021(10): 60-61.
[2] Ren, S., He, K., Girshick, R., et al. (2015) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Net-works. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149. [Google Scholar] [CrossRef] 
[3] Redmon, J., Divvala, S., Girshick, R., et al. (2016) You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 779-788. [Google Scholar] [CrossRef] 
[4] Redmon, J. and Farhadi, A. (2016) YOLO9000: Better, Faster, Stronger. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 6517-6525. [Google Scholar] [CrossRef] 
[5] Redmon, J. and Farhadi, A. (2018) YOLOv3: An In-cremental Improvement.
[6] Lehtinen, J., Munkberg, J., Hasselgren, J., et al. (2018) Noise2Noise: Learning Image Restoration without Clean Data. Proceedings of the 35th International Conference on Machine Learning, Stockholm, PMLR 80 2018.
[7] 刘迪, 贾金露, 赵玉卿, 钱育蓉. 基于深度学习的图像去噪方法研究综述[J]. 计算机工程与应用, 2021, 57(7): 1-13.
[8] An, X.H., Zhou, L., Liu, Z.G., Wang, C.Z., Li, P.F. and Li, Z.W. (2021) Dataset and Benchmark for Detecting Moving Objects in Construction Sites. Automation in Construction, 122, Article ID: 103482. [Google Scholar] [CrossRef]