浏览全部资源
扫码关注微信
Received：28 July 2020，
Revised：2021-1-12，
Accepted：19 January 2021，
Published：16 April 2022
移动端阅览
目的
2
绝缘子检测是输电线路智能巡维工作的重要组成部分，然而大多数情况仅能获得单一类型的绝缘子样本。将单一类型的绝缘子样本训练得到的模型直接用于其他类型的绝缘子检测，会由于训练数据与目标数据之间存在的域偏移导致其检测性能急剧下降。因此，提高模型的泛化能力以保持良好的检测性能显得尤为必要。为此，提出一种新颖的对抗一致性约束的无监督域自适应绝缘子检测算法。
方法
2
对源域样本与目标域样本分别设计了两个不同的分类器，并将网络的预测结果与对应的绝缘子进行类别约束，使模型能够提取到不同类型绝缘子独有的特征。此外，在对抗学习过程中引入一个额外的分类器用于将源域中绝缘子特征与从目标域中预测到的目标物特征分到同一类别下，从而使模型能提取不同类型绝缘子共有的鲁棒性特征。
结果
2
实验表明本文方法显著提高了模型的跨域检测性能。在glass → composite和composite → glass任务上的平均精度均值(mean average precision
mAP)分别达到55.1%和23.4%，优于主流的无监督域自适应目标检测方法。在公开数据集COCO(common objects in context)上的实验结果也较为优异，平均精度均值(mean average precision，mAP)达到61.5%。消融实验中，在glass → composite和composite → glass任务上，本文方法在基准性能上分别提升了11.5%和6.4%，表明了所提方法的有效性。
结论
2
本文方法减少了不同类型绝缘子间的差异带来的域偏移，提升了模型在跨域绝缘子检测任务中的泛化能力，提高了输电线路巡维工作的绝缘子检测效率。同时，在COCO数据集上的普适性实验表明本文方法同样适用于其他不同类物体的检测并且性能优异。
Objective
2
Insulator is widely used in overhead transmission line nowadays. It is a unique insulation device which can withstand voltage and mechanical stress. In order to reduce the potential safety hazards caused by insulator failures
overhead transmission lines need to be inspected regularly. It is necessary to detect insulators from the inspection images quickly and effectively in order to locate and analyze the defects. The electrical grid insulators applications are mainly divided into two categories: glass insulators and composite insulators. The color and shape are quite different for the two types of insulators
which results in a severe domain bias in the feature space. In most cases
we can only obtain the data for a single type of insulator and train the model by them. The detection of other types of insulators will cause the performance of the trained model to drop sharply due to the domain bias between the source data and the target data. Hence
it is required to improve the generalization ability of the model to maintain good detection performance. Unsupervised domain adaptation is a widely used method for cross-domain detection and recognition. This method uses labeled samples in the source domain and unlabeled samples in the target domain in the training process. A domain-invariant (or domain-aligned) feature representation learning method can effectively release the performance degradation caused by domain bias. Our demonstration illustrates an unsupervised domain adaptation insulator detection method to improve the efficiency of transmission line intelligent inspection and maintenance.
Method
2
In order to improve the model's generalization ability for insulators in the target domain in complicated transmission line images without the target domain labels
an unsupervised domain adaptation insulator detection algorithm is harnessed based on adversarial consistency constraint. The proposed algorithm is divided into two stages including pre-training and adversarial learning. In the pre-training stage
the labeled source domain samples and unlabeled target domain samples are fed into the network to extract features. The extracted two sets of features are input into two classification networks. The unique feature representation of two different types of insulators is obtained based on constraining the two classifiers with binary cross-entropy loss. The feature encoder and two classifiers are trained as well. In the process of adversarial consistency learning
an extra classifier is involved to obtain robustness feature representation. The features obtained by the source domain and target domain samples through the network are sent to a new initialized classification network
and the classifier is trained separately through binary cross-entropy to make the backbone unable to correctly classify the two features. The classifier is then fixed to train the backbone network
and the classification results of the two groups of features are limited to the same label. The network can extract the consistent and robust features of different types of insulators.
Result
2
This demonstration illustrates that our method significantly improves the cross-domain insulator detection performance
and the mean average precision (mAP) reaches 55.1% and 23.4% on the two tasks of glass → composite and composite → glass
respectively. The analyzed result of our method is qualified on the public dataset common objects in context (COCO). The mAP reaches 61.5%
which verifies our illustrated generality and extensibility. In the ablation study
the proposed mAP achieves 11.5% and 6.4% in benchmark performance improvement on the two tasks of glass → composite and composite → glass
respectively.
Conclusion
2
This method reduces the discrepancy-derived domain bias amongst various types of insulators. The generalization of the model is improved in cross-domain insulator detection tasks. Our method can improve the efficiency of the insulator detection in the transmission line inspection. The demonstrated results indicate that our method optimized unsupervised domain adaptation object detection methods. Both of the proposed loss functions can significantly improve the performance of the benchmark
which illustrates that the model is capable of learning a robustness feature representation. The COCO dataset is demonstrated for further verification.
Cao Y H, Chen K, Loy C C and Lin D H. 2020. Prime sample attention in object detection//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 11580-11588 [ DOI: 10.1109/CVPR42600.2020.01160 http://dx.doi.org/10.1109/CVPR42600.2020.01160 ]
Chen Y H, Li W, Sakaridis C, Dai D X and Van Gool L. 2018. Domain adaptive faster R-CNN for object detection in the wild//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 3339-3348 [ DOI: 10.1109/CVPR.2018.00352 http://dx.doi.org/10.1109/CVPR.2018.00352 ]
Cheng H Y, Han P, Wang D and Zhai Y J. 2017. Location method of insulators in power grid patrol aerial images. Journal of System Simulation, 29(6): 1327-1336
程海燕, 韩璞, 王迪, 翟永杰. 2017. 一种电网巡检航拍图像中绝缘子定位方法. 系统仿真学报, 29(6): 1327-1336 [DOI: 10.16182/j.issn1004731x.joss.201706023]
Everingham M, Van Gool L, Williams C K I, Winn J and Zisserman A. 2010. The PASCAL visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2): 303-338 [DOI: 10.1007/s11263-009-0275-4]
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press: 2672-2680
Guo C X, Fan B, Zhang Q, Xiang S M and Pan C H. 2020. AugFPN: improving multi-scale feature learning for object detection//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 12592-12601 [ DOI: 10.1109/CVPR42600.2020.01261 http://dx.doi.org/10.1109/CVPR42600.2020.01261 ]
Inoue N, Furuta R, Yamasaki T and Aizawa K. 2018. Cross-domain weakly-supervised object detection through progressive domain adaptation//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 5001-5009 [ DOI: 10.1109/CVPR.2018.00525 http://dx.doi.org/10.1109/CVPR.2018.00525 ]
Kim S, Choi J, Kim T and Kim C. 2019a. Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 6091-6100 [ DOI: 10.1109/ICCV.2019.00619 http://dx.doi.org/10.1109/ICCV.2019.00619 ]
Kim T, Jeong M, Kim S, Choi S and Kim C. 2019b. Diversify and match: a domain adaptive representation learning paradigm for object detection//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 12448-12457 [ DOI: 10.1109/CVPR.2019.01274 http://dx.doi.org/10.1109/CVPR.2019.01274 ]
Krizhevsky A, Sutskever I and Hinton G E. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90 [DOI: 10.1145/3065386]
Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P and Zitnick C L. 2014. Microsoft COCO: common objects in context//Proceedings of the 12th European Conference on Computer Vision. Zurich, Switzerland: Springer: 740-755 [ DOI: 10.1007/978-3-319-10602-1_48 http://dx.doi.org/10.1007/978-3-319-10602-1_48 ]
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot MultiBox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, the Netherlands: Springer: 21-37 [ DOI: 10.1007/978-3-319-46448-0_2 http://dx.doi.org/10.1007/978-3-319-46448-0_2 ]
Lu X, Li B Y, Yue Y X, Li Q Q and Yan J J. 2019. Grid R-CNN//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 7355-7364 [ DOI: 10.1109/CVPR.2019.00754 http://dx.doi.org/10.1109/CVPR.2019.00754 ]
Pang J M, Chen K, Shi J P, Feng H J, Ouyang W L and Lin D H. 2019. Libra R-CNN: towards balanced learning for object detection//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 821-830 [ DOI: 10.1109/CVPR.2019.00091 http://dx.doi.org/10.1109/CVPR.2019.00091 ]
Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 779-788 [ DOI: 10.1109/CVPR.2016.91 http://dx.doi.org/10.1109/CVPR.2016.91 ]
Redmon J and Farhadi A. 2018. YOLOv3: an incremental improvement [EB/OL]. [2020-06-28] . https://arxiv.org/pdf/1804.02767v1.pdf https://arxiv.org/pdf/1804.02767v1.pdf
RoyChowdhury A, Chakrabarty P, Singh A, Jin S, Jiang H Z, Cao L L and Learned-Miller E. 2019. Automatic adaptation of object detectors to new domains using self-training//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 780-790 [ DOI: 10.1109/CVPR.2019.00087 http://dx.doi.org/10.1109/CVPR.2019.00087 ]
Tao X, Zhang D P, Wang Z H, Liu X L, Zhang H Y and Xu D. 2020. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(4): 1486-1498 [DOI: 10.1109/TSMC.2018.2871750]
Yang S Q, Wu L, Wiliem A and Lovell B. 2020. Unsupervised domain adaptive object detection using forward-backward cyclic adaptation [EB/OL]. [2020-02-03] . https://arxiv.org/pdf/2002.00575.pdf https://arxiv.org/pdf/2002.00575.pdf
Yao C Y, Jin L J and Yan S J. 2012. Recognition of insulator string in power grid patrol images. Journal of System Simulation, 24(9): 1818-1822
姚春羽, 金立军, 闫书佳. 2012. 电网巡检图像中绝缘子的识别. 系统仿真学报, 24(9): 1818-1822 [DOI: 10.16182/j.cnki.joss.2012.09.024]
Zhang S F, Chi C, Yao Y Q, Lei Z and Li S Z. 2020. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 9756-9765 [ DOI: 10.1109/CVPR42600.2020.00978 http://dx.doi.org/10.1109/CVPR42600.2020.00978 ]
Zhao Z B, Cui Y P, Qi Y C, Du L Q, Zhang K and Zhai Y J. 2019. Detection method of insulator in aerial inspection image based on modified R-FCN. Computer Science, 46(3): 159-163
赵振兵, 崔雅萍, 戚银城, 杜丽群, 张珂, 翟永杰. 2019. 基于改进的R-FCN航拍巡线图像中的绝缘子检测方法. 计算机科学, 46(3): 159-163 [DOI: 10.11896/j.issn.1002-137X.2019.03.024]
Zhu J Y, Park T, Isola P and Efros A A. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2242-2251 [ DOI: 10.1109/ICCV.2017.244 http://dx.doi.org/10.1109/ICCV.2017.244 ]
相关文章
相关作者
相关机构
京公网安备11010802024621
