嵌入式设备高效卷积神经网络的电力设备检测
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摘要
随着大型图像集的出现以及计算机硬件尤其是GPU的快速发展,卷积神经网络(CNN)已经成为人工智能领域的一种成功算法,在各种机器学习任务中表现出色.但CNN的计算复杂度远高于传统算法,嵌入式设备上有限资源的限制成为制造高效嵌入式计算的挑战性问题.在本文中,我们提出了一种基于嵌入式设备的高效卷积神经网络用于电力设备检测,根据处理速度评估这种高效的神经网络.结果表明,该算法能够满足嵌入式设备实时视频处理的要求.
With the emergence of large image sets and the rapid development of computer hardware especially GPU,Convolutional Neural Network(CNN) has become a successful algorithm in the region of artificial intelligence and exhibit remarkable performance in various machine learning tasks. But the computation complexity of CNN is much higher than traditional algorithms, however, the restrict of limited resources on embedded devices become a challenging issue for making efficient embedded computing. In this study, we propose a efficient convolutional neural networks based on embedded devices for electrical equipment inspection, this efficient neural network is evaluated in term of processing speed. The results show that the proposed algorithm can meet the requirement of real-time video processing on embedded devices.
With the emergence of large image sets and the rapid development of computer hardware especially GPU,Convolutional Neural Network(CNN) has become a successful algorithm in the region of artificial intelligence and exhibit remarkable performance in various machine learning tasks. But the computation complexity of CNN is much higher than traditional algorithms, however, the restrict of limited resources on embedded devices become a challenging issue for making efficient embedded computing. In this study, we propose a efficient convolutional neural networks based on embedded devices for electrical equipment inspection, this efficient neural network is evaluated in term of processing speed. The results show that the proposed algorithm can meet the requirement of real-time video processing on embedded devices.
引文
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2Amantea R,Goodman LA,Pantuso FP,et al.Progress toward an uncooled IR imager with 5-mK NETD.SPIE's International Symposium on Optical Science,Engineering,and Instrumentation.San Diego,CA,USA.1998.647-660.
3Huda ASN,Taib S.Suitable features selection for monitoring thermal condition of electrical equipment using infrared thermography.Infrared Physics&Technology,2013,61:184-191.
4Jaffery ZA,Dubey AK.Design of early fault detection technique for electrical assets using infrared thermograms.International Journal of Electrical Power&Energy Systems,2014,63:753-759.
5门洪,于加学,秦蕾.基于CA和OTSU的电气设备红外图像分割方法.电力自动化设备,2011,31(9):92-95.[doi:10.3969/j.issn.1006-6047.2011.09.020]
6Korman S,Reichman D,Tsur G,et al.Fast-match:Fast affine template matching.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland,OR,USA.2013.2331-2338.
7Hinton GE,Osindero S,Teh YW.A fast learning algorithm for deep belief nets.Neural Computation,2006,18(7):1527-1554.[doi:10.1162/neco.2006.18.7.1527]
8Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation.Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus,OH,USA.2014.580-587.
9He KM,Zhang XY,Ren SQ,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.[doi:10.1109/TPAMI.2015.2389824]
10Girshick R.Fast R-CNN.Proceedings of 2015 IEEE International Conference on Computer Vision.Santiago,Chile.2015.1440-1448.
11Ren SQ,He KM,Girshick R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks.Proceedings of the 28th International Conference on Neural Information Processing Systems.Montreal,Canada.2015.91-99.
12Redmon J,Divvala S,Girshick R,et al.You only look once:Unified,real-time object detection.Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA.2016.779-788.
13Redmon J,Farhadi A.YOLO9000:Better,faster,stronger.arXiv preprint arXiv:1612.08242,2016.
14Han S,Mao HZ,Dally WJ.A deep neural network compression pipeline:Pruning,quantization,huffman encoding.arXiv preprint arXiv:1510.00149,2015.
15Howard AG,Zhu ML,Chen B,et al.MobileNets:Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861,2017.
16Sifre L.Rigid-motion scattering for image classification[Ph. D.thesis].Paris:Ecole Polytechnique,2014.
17Zhang XY,Zhou XY,Lin MX,et al.Shufflenet:An extremely efficient convolutional neural network for mobile devices.arXiv preprint arXiv:1707.01083,2017.
18Ioffe S,Szegedy C.Batch normalization:Accelerating deep network training by reducing internal covariate shift.arXiv preprint arXiv:1502.03167,2015.
19Sandler M,Howard A,Zhu ML,et al.MobileNetV2:Inverted residuals and linear bottlenecks.Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT,USA.2018.4510-4520.
20Liu W,Anguelov D,Erhan D,et al.SSD:Single shot MultiBox detector.arXiv preprint arXiv:1512.02325,2016.