[1] |
董翔宇, 季坤, 朱俊, 等. 对特高压变电站巡检机器人路径规划改进蚁群算法的研究[J]. 电力系统保护与控制, 2021, 49(18):154-160.
|
|
DONG Xiangyu, JI Kun, ZHU Jun, et al. A retrofitted ant colony algorithm for inspection robot path planning in UHV substations[J]. Power System Protection and Control, 2021, 49(18):154-160.
|
[2] |
黄山, 吴振升, 任志刚, 等. 电力智能巡检机器人研究综述[J]. 电测与仪表, 2020, 57(2):26-38.
|
|
HUANG Shan, WU Zhensheng, REN Zhigang, et al. Review of electric power intelligent inspection robot[J]. Electrical Measurement & Instrumentation, 2020, 57(2):26-38.
|
[3] |
黄健, 张钢. 深度卷积神经网络的目标检测算法综述[J]. 计算机工程与应用, 2020, 56(17):12-23.
|
|
HUANG Jian, ZHANG Gang. Survey of object detection algorithms for deep convolutional neural networks[J]. Computer Engineering and Applications, 2020, 56(17):12-23.
|
[4] |
伍艺佳, 华雄, 王丽蓉, 等. 基于注意力机制学习的变电设备缺陷检测方法[J]. 计算机与现代化, 2021(2):7-12, 17.
|
|
WU Yijia, HUA Xiong, WANG Lirong, et al. Method of substation equipment defect detection based on attention mechanism learning[J]. Computer and Modernization, 2021(2):7-12, 17.
|
[5] |
ZHANG A Z, HU X Y, JIN M Y, et al. Multi-target defect detection of railway track based on image processing[C]// 2020 Chinese Control and Decision Conference (CCDC). IEEE, 2020: 3377-3382.
|
[6] |
JABRI S, SAIDALLAH M, EL BELRHITI EL ALAOUI A, et al. Moving vehicle detection using haar-like, LBP and a machine learning adaboost algorithm[C]// 2018 IEEE International Conference on Image Processing, Applications and Systems. IEEE, 2018: 121-124.
|
[7] |
刘黎, 韩睿, 韩译锋, 等. 改进的Faster-RCNN目标检测方法在变电站悬挂异物检测中的应用[J]. 电测与仪表, 2021, 58(1):142-146.
|
|
LIU Li, HAN Rui, HAN Yifeng, et al. Application of an improved Faster-RCNN object detection method in the detection of suspended foreign matters in substation[J]. Electrical Measurement & Instrumentation, 2021, 58(1):142-146.
|
[8] |
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 and Machine Intelligence, 2017, 39(6):1137-1149.
doi: 10.1109/TPAMI.2016.2577031
URL
|
[9] |
CHEN X J, AN Z Y, HUANG L S, et al. Surface defect detection of electric power equipment in substation based on improved YOLOV4 algorithm[C]// 2020 10th International Conference on Power and Energy Systems (ICPES). Chengdu, China: IEEE, 2020: 256-261.
|
[10] |
李彬, 汪诚, 吴静, 等. 改进YOLOv4算法的航空发动机部件表面缺陷检测[J]. 激光与光电子学进展, 2021(14):406-415.
|
|
LI Bin, WANG Cheng, WU Jing, et al. Surface defect detection of aeroengine components based on improved YOLOv4 algorithm[J]. Laser & Optoelectronics Progress, 2021(14):406-415.
|
[11] |
王鑫, 傅强, 王林, 等. 知识图谱可视化查询技术综述[J]. 计算机工程, 2020, 46(6):1-11.
|
|
WANG Xin, FU Qiang, WANG Lin, et al. Survey on visualization query technology of knowledge graph[J]. Computer Engineering, 2020, 46(6):1-11.
|
[12] |
李稳安, 陈柳柳, 陈实. 基于注意力模型的多模态特征融合雷达知识推荐[J]. 重庆大学学报, 2021, 44(7):34-42.
|
|
LI Wenan, CHEN Liuliu, CHEN Shi. A multi-modal feature fusion radar knowledge recommendation method based on attention mode[J]. Journal of Chongqing University, 2021, 44(7):34-42.
|
[13] |
MA W Z, ZHANG M, CAO Y, et al. Jointly learning explainable rules for recommendation with knowledge graph[C]// The World Wide Web Conference on - WWW ‘19. New York: ACM Press, 2019.
|
[14] |
NIE B L, SUN S Q. Knowledge graph embedding via reasoning over entities, relations, and text[J]. Future Generation Computer Systems, 2019, 91:426-433.
doi: 10.1016/j.future.2018.09.040
URL
|
[15] |
SONG Q, WU Y H, LIN P, et al. Mining summaries for knowledge graph search[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(10):1887-1900.
doi: 10.1109/TKDE.2018.2807442
URL
|
[16] |
HUANG H C, HONG Z, ZHOU H M, et al. Knowledge graph construction and application of power grid equipment[J]. Mathematical Problems in Engineering, 2020, 2020:8269082.
|
[17] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 779-788.
|
[18] |
REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 6517-6525.
|
[19] |
REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. [2021-01-12]. https://arxiv.org/abs/1804.02767
|
[20] |
WANG C Y, MARK LIAO H Y, WU Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Seattle, WA, USA: IEEE, 2020: 1571-1580.
|
[21] |
HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916.
doi: 10.1109/TPAMI.2015.2389824
URL
|
[22] |
LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 936-944.
|
[23] |
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, UT, USA: IEEE, 2018:8759-8768.
|