在港口门机抓斗装卸干散货的作业过程中,人眼观察无法精确判断抓斗所在位置,会带来工作效率低下及安全性等问题。为解决该问题首次提出了一种基于深度学习的门机抓斗检测方法。利用改进的深度卷积神经网络YOLOv3-tiny对抓斗数据集进行训练及测试,进而学习其内部特征表示。实验结果表明,基于深度学习的门机抓斗检测方法可实现门机抓斗检测速度每秒45帧,召回率高达95.78%,在很好满足检测实时性与准确性的同时,提高了工业现场作业的安全性及效率。
基于深度学习的门机抓斗检测方法
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出版日期:2021年1月15日
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参考文献
[1] Chen Y M. Current situation and future trend of Chinese ports[J]. China Water Transp, 2019(6): 7.
陈英明. 中国港口现状及未来走势[J]. 中国水运, 2019(6): 7.
[2] Xing X J. Some thoughts on bulk cargo handling industry of domestic port[J]. Hoist Con Mach, 2019(10): 1.
邢小健. 对国内港口散货装卸行业的一些思考[J]. 起重运输机械, 2019(10): 1.
[3] Ji B S. A study on the design of grab bucket control program[J]. J Nantong Vocational Tech Shipping Coll, 2012, 11(4): 76–79.
季本山. 现代门机抓斗控制程序的设计[J]. 南通航运职业技术学院学报, 2012, 11(4): 76–79.
[4] Yao Z Y. Research on the application of object detection technology based on deep learning algorithm[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.
姚筑宇. 基于深度学习的目标检测研究与应用[D]. 北京: 北京邮电大学, 2019.
[5] Manana M, Tu C L, Owolawi P A. A survey on vehicle detection based on convolution neural networks[C]//Proceedings of the 3rd IEEE International Conference on Computer and Communications, 2017: 1751–1755.
[6] Dai W C, Jin L X, Li G N, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electron Eng, 2018, 45(12): 180350.
戴伟聪, 金龙旭, 李国宁, 等. 遥感图像中飞机的改进YOLOv3实时检测算法[J]. 光电工程, 2018, 45(12): 180350.
[7] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580–587.
[8] Girshick R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440–1448.
[9] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.
[10] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779–788.
[11] Redmon J, Farhadi A. YOLOv3: an incremental improvement[Z]. arXiv:1804.02767, 2018.
[12] He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Trans Pattern Anal Mach Intell, 2014, 37(9): 1904–1916.
[13] Howard A G, Zhu M L, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[Z]. arXiv:1704.04861, 2017.
[14] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936–944.
[15] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.
[16] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[Z]. arXiv:1502.03167, 2015.
[17] LeCun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Comput, 1989, 1(4): 541–551.
[18] Maas A L, Hannum A Y, Ng A Y. Rectifier nonlinearities improve neural network acoustic models[C]//Proceedings of the 30th International Conference on Machine Learning, 2013.
[19] Sandler M, Howard A, Zhu M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4510–4520.
[20] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[Z]. arXiv:1511.07122, 2015.
基金项目:
河北省自然科学基金资助项目(F2019203195)
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引用本文:
张文明, 刘向阳, 李海滨, 等. 基于深度学习的门机抓斗检测方法[J]. 光电工程, 2021, 48(1): 200062.