Area-based non-maximum suppression algorithm for multi-object fault detection

Abstract

Unmanned aerial vehicle (UAV) photography has become the main power system inspection method; however, automated fault detection remains a major challenge. Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously. The object detection method involving deep learning provides a new method for fault detection. However, the traditional non-maximum suppression (NMS) algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers. In this study, we propose an area-based non-maximum suppression (A-NMS) algorithm to solve the problem of one object having multiple labels. The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects. Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58% and 91.23%, respectively, in case of the aerial image datasets and realize multi-object fault detection in aerial images.

Keywords

fault detection / area-based non-maximum suppression (A-NMS) / cropping detection

References

[1] Sun J. Research on Diagnosis of Insulator Crack Based on Edge Detection. Beijing: North China Electric Power University, 2008 (in Chinses)

[2] Zhang F Y. Identification and Research of Abnormal Patrol Diagram of Transmission Line Based on Computer Vision. Changchun: Jilin University, 2015 (in Chinese)

[3] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507

[4] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of Conference on Neural Information Processing Systems, 2012, 1106–1114

[5] Simonyan K, Zisserman A. Very deep convolutional network for large-scale image recognition. In: Proceedings of IEEE International Conference of Learning Representation, 2015, 1–5

[6] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016, 2818–2826

[7] He K, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016, 770–778

[8] Lee K P, Wu B H, Peng S L. Deep-learning-based fault detection and diagnosis of air-handling units. Building and Environment, 2019, 157: 24–33

[9] Lin T Y, Dollar P, Girshick R, He K M. Feature pyramid networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017, 2117–2125

[10] Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of Conference on Neural Information Processing Systems, 2015, 91–99

[11] Yan W, Yu L. On accurate and reliable anomaly detection for gas turbine combustors: a deep learning approach. arXiv:1908.09238, 2019

[12] Luo B, Wang H, Liu H, Li B, Peng F. Early fault detection of machine tools based on deep learning and dynamic identification. IEEE Transactions on Industrial Electronics, 2019, 66(1): 509–518

[13] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y, Berg A C. SSD: single shot multibox detector. In: Proceedings of European Conference on Computer Vision, 2016, 21–37

[14] Cai Z, Vasconcelos N. Cascade R-CNN: delving into high quality object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018, 6154–6162

[15] Wang W G, Tian B, Liu Y, Liu L, Li J X. Research on power component identification of UAV inspection image based on RCNN. Journal of Earth Information Science, 2017, 2(19): 256–263

[16] Liu Y, Jin L, Zhang S, Sheng Z. Detecting curve text in the wild: new dataset and new solution. arXiv: 1712.02170, 2017

[17] Dai Y, Huang Z, Gao Y, Chen K. Fused text segmentation networks for multi-oriented scene text detection. In: Proceedings of the 24th International Conference on Pattern Recognition, 2018, 3604–3609

[18] Abdurashitov A, Lychagov V V, Sindeeva O A, Semyachkina-Glushkovskaya O V, Tuchin V V. Histogram analysis of laser speckle contrast image for cerebral blood flow monitoring. Frontiers of Optoelectronics, 2015, 8(2): 187–194

[19] Sudhakar M, Reddy V, Rao Y. Influence of optical filtering on transmission capacity in single mode fiber communications. Frontiers of Optoelectronics, 2015, 8(4): 424–430

[20] Huang G, Liu Z, Maaten L. Densely connected convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017, 4700–4708

[21] Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick C L. Microsoft COCO: common objects in context. In: Proceedings of European Conference on Computer Vision, 2014, 740–755

[22] Everingham M, Van Gool L, Williams C K I, Winn J, Zisserman A. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 2010, 88(2): 303–338

Acknowledgements

This paper was supported by the National Grid Corporation Headquarters Science and Technology Project: Key Technology Research, Equipment Development and Engineering Demonstration of Artificial Smart Drived Electric Vehicle Smart Travel Service (No. 52020118000G).

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2020 Higher Education Press