基于感受野模块的绝缘子实时识别定位方法
吉志朋1，张国伟1，卢秋红2
（1 上海电力大学 自动化工程学院，上海 200082；2 上海合时智能科技有限公司，上海 201100）
摘 要：针对使用无人机进行绝缘子识别实时性的要求，以感受野模块（RFB）网络为基础，提出了一种基于RFB模型改进的轻量型架构。使用MobileNetV3网络作为特征提取主干，设计了新的感受野模块RFB-X，并使用Focal-loss损失函数解决正负样本不平衡问题。实验结果表明，该模型提高了绝缘子的检测速度和准确率。
关键词：轻量型模型；感受野模块；无人机；绝缘子检测；实时性
中图分类号：TM216 文献标识码：A 文章编号：1007-3175(2020)09-0019-04
Real Time Detection of Insulator by RFB
JI Zhi-peng1, ZHANG Guo-wei1, LU Qiu-hong2
（1 School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200082, China;
2 Shanghai Heshi Intelligent Technology Co., Ltd, Shanghai 2011 00, China）
Abstract: In response to the real-time requirements of using UAVs for insulator identification, based on the receptive field module (RFB) network, a lightweight architecture based on the improvement of the RFB model is proposed. Firstly, mobile MobileNetV3 is used as the main feature extraction module, then a new receptive field module RFB-X is designed, and finally the Focal-loss function is used to solve the imbalance of positive and negative samples. Experiments show that the model improves the speed and accuracy of insulator detection.
Key words: lightweight model; RFB; UAV; insulator detection; real time
参考文献
[1] 吴涛，王伟斌，于力，等. 轻量级YOLOV3 的绝缘子缺陷检测方法[J]. 计算机工程，2019，45(8)：275-280.
[2] 张焕坤，李军毅，张斌. 基于改进型YOLO v3 的绝缘子异物检测方法[J]. 中国电力，2020，53(2)：49-55.
[3] REDMON J, FARHADI A.YOLOv3:An Incremental Improvement[EB/OL].(2018-04-08)[2020-05-25]. https://arxiv.org/pdf/1804.02767.pdf.
[4] HOWARD A, ZHU M, CHEN B, et al.MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[EB/OL].(2017-04-17) [2020-05-25].https://arxiv.org/pdf/1704.04861.pdf.
[5] 陈俊杰，叶东华，产焰萍，等. 基于Faster R-CNN 模型的绝缘子故障检测[J]. 电工电气，2020(4)：56-60.
[6] REN S, HE K, GIRSHICK R, et al.Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[C]//Neural Information Processing Systems，2015：91-99.
[7] 李伟性，郑武略，王宁，等. 基于S S D 算法的输电线路上绝缘子缺陷检测方法研究[J]. 仪器仪表用户，2019，26(8)：1-4.
[8] LIU S, HUANG D, WANG Y, et al.Receptive Field Block Net for Accurate and Fast Object Detection[C]//European Conference on Computer Vision，2018：404-419.
[9] HOWARD A, SANDLER M, CHU G, et al.Searching for MobileNetV3[EB/OL].(2019-11-20)[2020-05-25].https: //arxiv.org/pdf/1905.02244.pdf.
[10] LIN T, GOYAL P, GIRSHICK R, et al.Focal Loss for Dense Object Detection[C]//International Conference on Computer Vision，2017：2999-3007.
[11] SANDLER M, HOWARD A, ZHU M, et al.MobileNetV2: Inverted Residuals and Linear Bottlenecks[C]//Computer Vision and Pattern Recognition，2018：4510-4520.
[12] ZHANG X, ZHOU X, LIN M, et al.ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices[C]//Computer Vision and Pattern Recognition，2018：6848-6856.
[13] MA N, ZHANG X, ZHENG H, et al.ShuffleNetV2: Practical Guidelines for Efficient CNN Architecture Design[C]//European Conference on Computer Vision，2018：122-138.
[14] HU J, SHEN L, ALBANIE S, et al.Squeeze-and-Excitation Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2020，42(8)：2011-2023.
[15] QIN Z, LI Z, ZHANG Z, et al.ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices[C]//International Conference on Computer Vision，2019：6717-6726.
[16] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning[C]//National Conference on Artificial Intelligence，2016：4278-4284.
[17] CHOLLET F.Xception: Deep Learning with Depthwise Separable Convolutions[C]//Computer Vision and Pattern Recognition，2017：1800-1807.
[18] LIU W, ANGUELOV D, ERHAN D, et al.SSD: Single Shot MultiBox Detector[C]//European Conference on Computer Vision，2016：21-37.
[19] SHRIVASTAVA A, GUPTA A, GIRSHICK R, et al. Training Region-Based Object Detectors with Online Hard Example Mining[C]//Computer Vision and Pattern Recognition，2016：761-769.
