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ID-Net: an improved mask R-CNN model for intrusion detection under power grid surveillance

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Abstract

Intrusion detection is a crucial task in power grid surveillance system by providing early warning for power grid security. Construction machinery and engineering vehicles, as the most common intrusion objects, have become the major concern for preventing external damages in power grid maintenance. In this paper, by considering the diversity of scales of intrusion objects and complexity of application scenarios under power grid surveillance, we compiled a dataset which contains 8177 images captured by 653 different power grid surveillance cameras. Based on this dataset, we proposed an improved context-aware mask region-based convolutional neural network (Mask R-CNN) model, namely ID-Net, for intrusion object detection. A modulated deformable convolutional operation is integrated into the backbone network for learning robust feature representations from geometric variations in engineering vehicles. By considering the correlation between objects and their context, a self-attention-based module is leveraged for long-range context relation modeling. For small objects detection, a feature integration module is applied for multi-scale feature fusion under a pyramid hierarchy. Then, a cascaded coarse-to-fine region proposal network is incorporated for progressively refining the bounding box location regression. Experimental results have demonstrated that our model can achieve competitive performance in comparison with state-of-the-art object detection methods.

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Acknowledgements

This work was supported in part by the National Key R&D Program of China under Grant Nos. 2018YFB1003800, 2018YFB1003805, the National Natural Science Foundation of China under Grant No. 61572156 and No. 61832004, the Shenzhen Science and Technology Program under Grant No. JCYJ20170413105929681 and the project of State Grid Shaanxi Electrical Power Company under Grant No. FWZ-ZB-GWSNDL20-02-86 and No. 5226KY19004Y.

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Authors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, 710049, China

    Feng Gao, Shengchang Ji & Jie Guo

  2. State Grid Shaanxi Electric Power Research Institute, Xi’an, 710100, China

    Feng Gao, Haokun Wei & Nan Wang

  3. Harbin Institute of Technology, Shenzhen, Shenzhen, 518005, China

    Qun Li, Yuzhu Ji & Biao Yang

  4. State Grid Xi’an Electric Power Supply Company, Xi’an, 710032, China

    Yang Liu

  5. State Grid Shaanxi Electric Power Company, Xi’an, 710054, China

    Simeng Feng

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  1. Feng Gao
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  2. Shengchang Ji
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  3. Jie Guo
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  4. Qun Li
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  5. Yuzhu Ji
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  7. Simeng Feng
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  8. Haokun Wei
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  9. Nan Wang
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  10. Biao Yang
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Correspondence to Yuzhu Ji or Biao Yang.

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Gao, F., Ji, S., Guo, J. et al. ID-Net: an improved mask R-CNN model for intrusion detection under power grid surveillance. Neural Comput & Applic 33, 9241–9257 (2021). https://doi.org/10.1007/s00521-021-05688-2

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  • DOI: https://doi.org/10.1007/s00521-021-05688-2

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