基于旋转框定位的绝缘子污秽检测算法

doi:10.3778/j.issn.1002-8331.2208-0163

摘要/Abstract

摘要： 为解决当前无人机巡检污秽绝缘子过程中受光照强弱影响大、背景复杂造成检测准确率低以及水平框并不能准确定位绝缘子等问题，提出一种改进R3Det的绝缘子污秽细粒度旋转目标检测算法。在特征提取部分，使用ConvNeXt做为主干特征提取网络，实现对绝缘子污秽细粒度特征的增强提取；同时采用PANet特征融合网络关联不同感受野特征。在检测头网络部分，使用对齐卷积和小尺度卷积，提升模型的检测性能以及增加分类的准确性；并利用Kullback-Leibler Divergence(KLD)优化损失函数，改善带有旋转角度信息的污秽绝缘子检测框的定位精确。实验结果表明，改进后的算法在自制绝缘子污秽数据集上的mAP可以达到90.6%，相较于原始网络提高了4.9个百分点，同时模型计算量降低了25.2%，能够准确有效地识别并定位出输电线路中的污秽绝缘子。

关键词: 无人机图像, 绝缘子污秽, 旋转目标检测

Abstract: An improved R3Det algorithm for fine-grained rotating target detection of contamination insulators is aiming at the problem that the current UAV inspection of fouled insulators is greatly influenced by the light intensity and low detection accuracy due to the complex background and the horizontal frame does not accurately locate the insulators is proposed. For the backbone of the network, ConvNeXt is used as the backbone feature extraction network to realize enhanced extraction of fine-grained features of insulator contamination. Meanwhile, the PANet is used to associate different receptive field features at the same time. In the part of detection head, aligned convolution and small-scale convolution are used to improve the detection performance of the model and increase the accuracy of classification. Kullback Leibler divergence（KLD） is also used to optimize the loss function to improve the positioning accuracy of the contaminated insulator detection frame with rotation angle information. The experimental results show that the mAP of the improved algorithm on the self-made insulator pollution data set can reach 90.6%, which is 4.9 percentage points higher than the original network, and the calculation amount of the model is reduced by 25.2%, which can accurately and effectively identify and locate the contaminated insulators in the transmission lines.

Key words: UAV image, insulator contamination, rotating object detection

王新良, 纪昂志, 李自强. 基于旋转框定位的绝缘子污秽检测算法[J]. 计算机工程与应用, 2023, 59(21): 319-326.

WANG Xinliang, JI Angzhi, LI Ziqiang. Insulator Contamination Detection Algorithm Based on Rotating Frame Location[J]. Computer Engineering and Applications, 2023, 59(21): 319-326.

参考文献

[1] 邱彦，郭裕钧，张血琴，等.基于高光谱技术的绝缘子污秽成分识别方法[J].高电压技术，2020，46（11）：4023-4030.
QIU Y，GUO Y J，ZHANG X Q，et al.Identification method of insulator pollution components based on hyperspectral technology[J].High Voltage Engineering，2020，46（11）：4023-4030.
[2] 付鹏，姚建刚，龚磊.利用红外特征和Softmax回归识别绝缘子污秽等级[J].计算机工程与应用，2015，51（13）：181-185.
FU P，YAO J G，GONG L.Contamination grades recognition of insulators using infrared features and Softmax regression[J].Computer Engineering and Applications，2015，51（13）：181-185.
[3] 罗潇，於锋，彭勇.基于深度学习的无人机电网巡检缺陷检测研究[J].电力系统保护与控制，2022，50（10）：132-140.
LUO X，YU F，PENG Y.UAV power grid inspection defect detection based on deep learning[J].Power System Protection and Control，2022，50（10）：132-140.
[4] 顾晓东，唐丹宏，黄晓华.基于深度学习的电网巡检图像缺陷检测与识别[J].电力系统保护与控制，2021，49（5）：91-97.
GU X D，TANG D H，HUANG X H.Deep learning-based defect detection and recognition of a power grid inspection image[J].Power System Protection and Control，2021，49（5）：91-97.
[5] 李彩林，张青华，陈文贺，等.基于深度学习的绝缘子定向识别算法[J].电子与信息学报，2020，42（4）：1033-1040.
LI C L，ZHANG Q H，CHEN W H，et al.Insulator orientation detection based on deep learning[J].Journal of Electronics & Information Technology，2020，42（4）：1033-1040.
[6] DING J，XUE N，LONG Y，et al.Learning roi transformer for oriented object detection in aerial images[C]//2019 IEEE Conference on Computer Vision and Pattern Recognition.Long Bench：IEEE，2019：2849-2858.
[7] YANG X，YANG J R，YAN J C，et al.SCRDet：towards more robust detection for small，cluttered and rotated objects[C]//2019 IEEE/CVF International Conference on Computer Vision.South Korea：IEEE，2019：8232-8241.
[8] XU Y C，FU M T，WANG Q M，et al.Gliding vertex on the horizontal bounding box for multi oriented object detection[J].IEEE Transactionson Pattern Analysis and Machine Intelligence，2020，43（4）：1452-1459.
[9] YANG X，YAN J C，HE T.On the arbitrary-oriented object detection：classification based approaches revisited[EB/OL].[2022-07-16].https：//arxiv.org/abs/2003.05597v3.
[10] YANG X，YAN J C，FENG Z M，et al.R3Det：refined single-stage detector with feature refinement for rotating oject[EB/OL].[2022-07-16].https：//arxiv.org/abs/1908.05612v6.
[11] HAN J M，DING J，LI J，et al.Align deep features for oriented object detection[J].IEEE Transactions on Geoscience and Remote Sensing，2022（60）：1-11.
[12] 曲优，李文辉.基于锚框变换的单阶段旋转目标检测[J].吉林大学学报（工学版），2022，52（1）：162-173.
QU Y，LI W H.Single-stage rotated object detection network based on anchor transformation[J].Journal of Jilin University（Engineering and Technology Edition），2022，52（1）：162-173.
[13] YANG Z，LIU S H，HU H，et al.RepPoints：point set representation for object detection[EB/OL].[2022-07-16].https：//arxiv.org/abs/1904.11490.
[14] 徐昌贵，张波，高建威，等.FCOSR：一种无锚框的SAR图像任意朝向船舶目标检测网络[J].雷达学报，2022，11（3）：335-346.
XU C G，ZHANG B，GAO J W，et al.FCOSR：an anchor-free method for arbitrary-oriented ship detection in SAR images[J].Journal of Radars，2022，11（3）：335-346.
[15] 王明阳，王江涛，刘琛.基于关键点的遥感图像旋转目标检测[J].电子测量与仪器学报，2021，35（6）：102-108.
WANG M Y，WANG J T，LIU C.Remote sensing image rotation object detection based on key points[J].Journal of Electronic Measurement and Instrumentation，2021，35（6）：102-108.
[16] YANG X，YAN J C，MING Q，et al.Rethinking rotated object detection with gaussian wasserstein distance loss[EB/OL].[2022-07-16].https：//arxiv.org/abs/2101.11952.
[17] YANG X，YANG X J，YANG J R，et al.Learning high-precision bounding box for rotated object detection via Kullback-Leibler divergence[EB/OL].[2022-07-16].https：//arxiv.org/abs/2106.01883.
[18] YANG X，ZHOU Y，ZHANG G F，et al.The KFIoU loss for rotated object detection[EB/OL].[2022-07-16].https：//arxiv.org/abs/2201.12558.
[19] LIN S，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision（ICCV），October 22-29，2017，Venice，Italy.New York：IEEE，2017：2980-2988.
[20] XIA G S，BAI X，DING J，et al.DOTA：a large scale dataset for object detection in aerial images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City：IEEE，2018：3974-3983.
[21] LIU Z，MAO H Z，WU C Y，et al.A ConvNet for the 2020s[EB/OL].[2022-07-16].https：//arxiv.org/abs/2201.03545.
[22] LIU S，QI L，QIN H F，et al.Path aggregation network for instance segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City：IEEE，2018：8759-8768.
[23] HE K M，ZHANG X Y，REN S Q，et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[24] LIU Z，LIN Y T，CAO Y，et al.Swin transformer：hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2021：10012-10022.
[25] LIN T，DOLLAR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of IEEE Conferenceon Computer Vision and Pattern Recognition.USA：IEEE，2017：936-944.
[26] REN S Q，HE K M，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence，2015，39（6）：1137-1149.