Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 290-294.doi: 10.11896/jsjkx.201200113

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Research on Rockfall Detection Method of Mountain Railway Slope Based on YOLOv3 Algorithm

LIU Lin-ya, WU Song-ying, ZUO Zhi-yuan, CAO Zi-wen   

  1. School of Civil Architecture,East China Jiaotong University,Railway Noise and Vibration Environment Engineering Research Center of the Ministry of Education,Nanchang 330013,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:LIU Lin-ya,born in 1973,professor,doctoral supervisor.His main research interests include railway noise and vibration environment.
    WU Song-ying,born in 1997,master.His main research interests include deep learning and so on.
  • Supported by:
    National Natural Science Foundation of China(51578238,51968025) and Key Program of Natural Science Foundation of Jiangxi Province of China(20192ACBL2009).

PDF (PC)

1260

Abstract: The existing detection methods have the disadvantages of high detection cost and complex operation.In view of the above problems,this paper proposes to use smart phones and civilian cameras combined with light compensation device to collect various rock samples of various sizes and shapes in mountainous areas,and use deep convolution network to learn and extract the corresponding characteristics of rock samples for training.At the same time,yolov3 algorithm is introduced to build the depth learning model of slope rockfall detection of mountain railway,so as to realize the real-time detection of slope rockfall along the mountain railway.In addition,fast RCNN algorithm is set as a parallel comparative experiment.The experimental results show that the two detection algorithms can achieve high detection accuracy.Compared with fast RCNN algorithm,the detection accuracy of yolov3 algorithm is relatively low,but it is more sensitive to the small rockfall target,more capturing,and the detection speed is faster,which can better meet the actual engineering needs.

Key words: Deep learning, Slope rockfall, Smart phone, Transfer learning

CLC Number: 

  • TP391
[1]DENG C.Research and application of fiber Bragg grating sensing technology for monitoring dangerous rock and rockfall on Yiwan railway slope[J].Railway Communication and Signal Engineering Technology,2012,9(2):27-30.
[2]WANG J,YE M,MA F S,et al.Design and implementation of collapse and rockfall monitoring and early warning system based on video image recognition[J].Journal of Applied Basic and Engineering Sciences,2014,22(5):952-963.
[3]SU J L,CHEN Y X,HONG X L.An Application of Object Detection Based on YOLOv3 in Traffic[C]//Proceedings of the 2019 International Conference on Image,Video and Signal Processing.2019:68-72.
[4]YU N T,GUO D Y,ZHE W,et al.Apple detection duringdifferent growth stages in orchards using the improved YOLO-V3 model[J].Computers and Electronics in Agriculture,2019(157):417-426.
[5]BILEL B,TAHA K,ANIS K,et al.Car Detection usingUnmanned Aerial Vehicles:Comparison between Faster R-CNN and YOLOv3[C]//Computer Vision and Pattern Recognition (cs.CV).2019.
[6]LIU C,GUO L J,ZHANG R,et al.Application of improved YOLOv3 algorithm in container number location[J].Sensors and Microsystems,2019,38(7):157-160.
[7]GAO Q,LIAN Q W.Insulator target detection in aerial images[J].Electrical Measurement and Instrumentation,2019,56(5):119-123.
[8]WU T,WANG W B,YU L,et al.Insulator defect detection method of lightweight YOLOv3[J].Computer Engineering,2019,45(8):275-280.
[9]LIU B,WANG S Z,ZHAO J S,et al.Ship tracking recognition based on Darknet network and YOLOv3 algorithm[J].Computer Application,2019,39(6):1663-1668.
[10]LI Y P,HOU L Y,WANG C.Moving object detection in automatic driving based on YOLOv3[J].Computer engineering and design,2019,40(4):1139-1144.
[11]ZHANG F K,YANG F,LI C.Fast vehicle detection methodbased on improved YOLOv3[J].Computer Engineering and Application,2019,55(2):12-20.
[12]JING L I,HUANG S,UNIVERSITY S,et al.YOLOv3 Based Object Tracking Method[J].Electronics Optics & Control,2019,33(1):15-23.
[13]XUE J L,DAI J G,ZHAO Q Z,et al.Weed detection in cotton field based on low altitude UAV image and YOLOv3[J].Journal of Shihezi University (Natural Science Edition),2019,37(1):21-27.
[14]YU N T,GUO D Y,ZHE W,et al.Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOv3-Dense[J].Journal of Sensors,2019,76(3):926-938.
[15]MORTEN B J,KAMAL N,THOMAS B M.Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data[C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.2017:9-15.
[16]XIE L,AHMAD T,JIN L,et al.A New CNN-Based Method for Multi-Directional Car License Plate Detection[J].IEEE Transactions on Intelligent Transportation Systems,2018,27(8):1-11.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[3] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[4] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[5] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[6] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[7] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[8] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[9] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[10] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[11] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[12] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[13] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[14] WANG Jun-feng, LIU Fan, YANG Sai, LYU Tan-yue, CHEN Zhi-yu, XU Feng. Dam Crack Detection Based on Multi-source Transfer Learning [J]. Computer Science, 2022, 49(6A): 319-324.
[15] CHU Yu-chun, GONG Hang, Wang Xue-fang, LIU Pei-shun. Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4 [J]. Computer Science, 2022, 49(6A): 337-344.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.