[1]ZHAO Zhenbing,GUO Guangxue,WANG Yiheng,et al.Rust detection in transmission line fittings via fusion of edge perception and statistical texture knowledge[J].CAAI Transactions on Intelligent Systems,2024,19(5):1228-1237.[doi:10.11992/tis.202306009]
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Rust detection in transmission line fittings via fusion of edge perception and statistical texture knowledge

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1228-1237 Column: 学术论文—智能系统 Public date: 2024-09-05
Title:
Rust detection in transmission line fittings via fusion of edge perception and statistical texture knowledge
Author(s):
ZHAO Zhenbing1;  GUO Guangxue1;  WANG Yiheng1;  ZHAO Wenqing2;  ZHAI Yongjie2
1. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
2. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
target detection; semantic segmentation; transmission lines; rust detection; fitting; attention mechanism; statistical textures; edge perception; knowledge fusion.
CLC:
TP183
DOI:
10.11992/tis.202306009
Abstract:
To address issues such as small target sizes, complex background environments, and irregular rust areas, a new rust detection algorithm combining edge sensing and statistical texture knowledge is proposed. First, the YOLOv7 model is improved for detecting fittings. Furthermore, the enhanced Res-UNet model is used for corrosion segmentation on the detected fittings. Additionally, squeeze excitation is incorporated to improve the stability of the model. The statistical texture knowledge module and edge-aware module are introduced, and a knowledge fusion module is proposed to integrate edge perception with statistical texture knowledge to enhance the precision of rust segmentation. Experimental results show that the detection and segmentation models increased by 2.8 percentage points and 7.7 percentage points, respectively.
References:
[1] 赵振兵, 蒋志钢, 李延旭, 等. 输电线路部件视觉缺陷检测综述[J]. 中国图象图形学报, 2021, 26(11): 2545-2560.
ZHAO Zhenbing, JIANG Zhigang, LI Yanxu, et al. Overview of visual defect detection of transmission line components[J]. Journal of image and graphics, 2021, 26(11): 2545-2560.
[2] 宋伟, 左丹, 邓邦飞, 等. 高压输电线防震锤锈蚀缺陷检测[J]. 仪器仪表学报, 2016, 37(S1): 113-117.
SONG Wei, ZUO Dan, DENG Bangfei, et al. Corrosion defect detection of earthquake hammer for high voltage transmission line[J]. Chinese journal of scientific instrument, 2016, 37(S1): 113-117.
[3] 戴玉静, 吕东辉, 郭松鸽. 基于颜色和纹理特征的输电线路锈蚀区域检测[J]. 工业控制计算机, 2018, 31(9): 39-40,43.
DAI Yujing, LYU Donghui, GUO Songge. Transmission line rusted area detection scheme based on color and texture features[J]. Industrial control computer, 2018, 31(9): 39-40,43.
[4] HUANG Xinbo, ZHANG Xiaoling, ZHANG Ye, et al. A method of identifying rust status of dampers based on image processing[J]. IEEE transactions on instrumentation and measurement, 2020, 69(8): 5407-5417.
[5] 王红星, 陈玉权, 沈杰, 等. 一种新型半监督极限学习机及其在防震锤锈蚀检测中的应用[J]. 计算机科学, 2020, 47(12): 262-266.
WANG Hongxing, CHEN Yuquan, SHEN Jie, et al. Novel Semi-supervised extreme learning machine and its application in Anti-vibration hammer corrosion detection[J]. Computer science, 2020, 47(12): 262-266.
[6] 刘军, 孙庆, 刘玮, 等. 基于U-Net网络和椭圆度量学习的防震锤锈蚀识别[J]. 计算机技术与发展, 2020, 30(11): 163-167.
LIU Jun, SUN Qing, LIU Wei, et al. Identification of Anti-vibration hammer corrosion of high-voltage transmission lines based on U-Net network and elliptic metric learning[J]. Computer technology and development, 2020, 30(11): 163-167.
[7] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
[8] 李辉, 钟平, 戴玉静, 等. 基于深度学习的输电线路锈蚀检测方法的研究[J]. 电子测量技术, 2018, 41(22): 54-59.
LI Hui, ZHONG Ping, DAI Yujing, et al. Study on detection method of transmission line rusty based on deep learning[J]. Electronic measurement technology, 2018, 41(22): 54-59.
[9] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
[10] 汤踊, 韩军, 魏文力, 等. 深度学习在输电线路中部件识别与缺陷检测的研究[J]. 电子测量技术, 2018, 41(6): 60-65.
TANG Yong, HAN Jun, WEI Wenli, et al. Research on part recognition and defect detection of trainsmission line in deep learning[J]. Electronic measurement technology, 2018, 41(6): 60-65.
[11] ATHA D J, JAHANSHAHI M R. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection[J]. Structural health monitoring, 2018, 17(5): 1110-1128.
[12] CHEN Fuchen, JAHANSHAHI M R. NB-CNN: deep learning-based crack detection using convolutional neural network and Na?ve Bayes data fusion[J]. IEEE transactions on industrial electronics, 2018, 65(5): 4392-4400.
[13] SONG Zhiwei, HUANG Xinbo, JI Chao, et al. Deformable YOLOX: detection and rust warning method of transmission line connection fittings based on image processing technology[J]. IEEE transactions on instrumentation and measurement, 2023, 72: 1-21.
[14] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 7464-7475.
[15] YANG Lingxiao, ZHANG Ruyuan , LI Lida, et al. SimAM: a simple, parameter-free attention module for convolutional neural networks[C]//International conference on machine learning. New York: PMLR, 2021: 11863-11874.
[16] TONG Zanjia, CHEN Yuhang, XU Zewei, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[EB/OL]. (2023-01-24)[2023-06-06]. http://arxiv.org/abs/2301.10051.
[17] SUNKARA R, LUO Tie. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects[C]//Machine Learning and Knowledge Discovery in Databases. Cham: Springer, 2023: 443-459.
[18] XIAO Xiao, LIAN Shen, LUO Zhiming, et al. Weighted res-UNet for high-quality retina vessel segmentation[C]// 2018 9th International Conference on Information Technology in Medicine and Education. Hangzhou: IEEE, 2018: 327-331.
[19] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.
[20] ZHU Lanyun, JI Deyi, ZHU Shiping, et al. Learning statistical texture for semantic segmentation[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 12532-12541.
[21] SUN Yujia, WANG Shuo, CHEN C, et al. Boundary- guided camouflaged object detection[EB/OL]. (2022-07-02)[2023-06-06]. http://arxiv.org/abs/2207.00794.
[22] 赵文清, 程幸福, 赵振兵, 等. 注意力机制和Faster RCNN相结合的绝缘子识别[J]. 智能系统学报, 2020, 15(1): 92-98.
ZHAO Wenqing, CHENG Xingfu, ZHAO Zhenbing, et al. Insulator recognition based on attention mechanism and Faster RCNN[J]. CAAI transactions on intelligent systems, 2020, 15(1): 92-98.
[23] FUKUI A, PARK D H, YANG D, et al. Multimodal compact bilinear pooling for visual question answering and visual grounding[EB/OL]. (2016-06-06)[2023-06-06]. http://arxiv.org/abs/1606.01847.
[24] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[25] FARHADI A, REDMON J. YOLOv3: an incremental improvement[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1804-2767.
[26] BOCHKOVSKIY A, WANG C Y, LIAO H M, et al. YOLOv4: optimal speed and accuracy of object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 2-7.
[27] SUN Peize, ZHANG Rufeng, JIANG Yi, et al. Sparse R-CNN: end-to-end object detection with learnable proposals[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 14449-14458.
[28] ZHU Xizhou, SU Weijie, LU Lewei, et al. Deformable DETR: deformable transformers for end-to-end object detection[EB/OL]. (2020-10-08)[2023-06-06]. http://arxiv.org/abs/2010.04159.
[29] LIU Shilong, LI Feng, ZHANG Hao, et al. DAB-DETR: dynamic anchor boxes are better queries for DETR[EB/OL]. (2022-01-28)[2023-06-06]. http://arxiv.org/abs/2201.12329.
[30] ZHAO Hengshuang, SHI Jianping, QI Xiaojuan, et al. Pyramid scene parsing network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 6230-6239.
[31] CHEN L C, ZHU Yukun, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//European Conference on Computer Vision. Cham: Springer, 2018: 833-851.
[32] HUANG Lang, YUAN Yuhui, GUO Jianyuan, et al. Interlaced sparse self-attention for semantic segmentation[EB/OL]. (2019-07-29)[2023-06-06]. http://arxiv.org/abs/1907.12273.
[33] YU Changqian, GAO Changxin, WANG Jingbo, et al. BiSeNet V2: bilateral network with guided aggregation for real-time semantic segmentation[J]. International journal of computer vision, 2021, 129(11): 3051-3068.
[34] XIE Enze, WANG Wenhai, YU Zhiding, et al. SegFormer: simple and efficient design for semantic segmentation with transformers[J]. Advances in neural information processing systems, 2021, 34: 12077-12090.
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