Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231200063-7.doi: 10.11896/jsjkx.231200063

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Study on Detection Method of Bridge Crack Based on YOLOv5

LI Jun1, LIU Nian2, ZHANG Shiyi2   

  1. 1 School of Mechatronics and Vehicle Enginerring,Chongqing Jiaotong University,Chongqing 400074,China
    2 School of shipping and Naval Architecture,Chongqing JiaotongUniversity,Chongqing 400074,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:LI Jun,born in 1964,Ph. D，professor, master's supervisor.His main research interests include energy saving and new energy vehicles and their applications,intelligent vehicle perception，decision making and control, automobile engine combustion emissions and control, advanced manufacturing technology and application.
    LIU Nian,born in 1999,postgraduate.His main research interests include bridge damage crack identification and detection,and so on.
  • Supported by:
    National Natural Science Foundation of China (51305472) and Chongqing Key Laboratory of Urban Rail Transit Vehicle System Integration and Control Fund Project.

PDF (PC)

168

Abstract: To address the issues of different crack recognition in bridge crack identification,improve the model's fitting ability,and enhance crack feature extraction capability,this paper proposes an algorithm called YOLOv5-Crack based on the fusion of YOLOv5 and EfficientNet,incorporating the CBAM attention mechanism in bridge crack recognition.Firstly,the feature extraction network of YOLOv5 is replaced with the EfficientNet network known for its high accuracy and efficiency,to extract crack features.Secondly,the convolutional block attention module(CBAM) is used to enhance the model's ability to capture the feature information of shallow targets by combining channel and spatial attention modules,thereby improving crack recognition accuracy.Finally,the model is trained on the bridge crack dataset “concrete crack images for classification”.The research results show that YOLOv5-Crack demonstrates higher accuracy in detecting large cracks compared to YOLOv5,with improved mAP@0.5,recall,and precision.Additionally,it consumes less computing power while meeting the requirements of crack detection.

Key words: YOLOv5, EfficientNet, Crack detection, CBAM

CLC Number: 

  • U448.33
[1]WANG X Q.Fatigue crack detection and evaluation of steel structure bridges based on AC electromagnetic field detection technology[J].Industrial Architecture,2023,53(8):102-106.
[2]LIU X,GAO S W,HE Y.Bridge Crack Detection and Recognition Based on Convolutional Neural Network Transfer Learning [J].Science and Technology Innovation Herald,2019(4):24-25.
[3]SHAO Z X.Research and Implementation of Concrete Ultrasonic Testing Technology [J].Vibration,Testing and Diagnosis,2012,32(3):397-401,513.
[4]DENG Z Y,KANG Y H,ZHANG J K,et al.Multi-source effect in magnetizing-based eddy current testing sensor for surface crack in ferromagnetic materials[J].Sensors & Actuators:A.Physical,2018,271.
[5]QUANG T L,NAOYA K,KOUICHI S,et al.Eddy current convergence probes with self-differential and self-nulling characteristics for detecting cracks in conductive materials[J].Sensors and Actuators:A.Physical,2023,349.
[6]ZHANG X Y,ZHOU B,LI H,et al.Depth detection of spar cap defects in large-scale wind turbine blades based on a 3D heat conduction model using step heating infrared thermography[J].Measurement Science and Technology,2022,33(5).
[7]DU Y C,ZHANG X M,LI F,et al.Detection of Crack Growth in Asphalt Pavement Through Use of Infrared Imaging[J].Transportation Research Record Journal of the Transportation Research Board,2017,2645:24-31.
[8]TANG L,JIANG J P,GU P Y,et al.Experimental study on ultrasonic infrared detection of concrete components[J].Journal of Hydraulic Engineering,2012,43(Z1):70-75.
[9]XU Y,ZHANG H Y,WANG Q Y.Experimental study on microcrack detection of heat-damaged concrete based on nonlinear ultrasonic technology[J].Journal of Vibration and Shock,2021,40(5):126-135.
[10]NITHIN K,RAVIRAJ V,RENGASWAM Y J.Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V[J].Metals,2021,12(1).
[11]ZHANG L,WANG Z C,WANG L,et al.Machine learning-based real-time visible fatigue crack growth detection[J].Digital Communications and Networks,2021,7(4).
[12]MIR B A,SASAKI T,NAKAO K,et al.Machine learning-based evaluation of the damage caused by cracks on concrete structures[J].Precision Engineering,2022,76.
[13]WANG C L,HOU X L,LIU Y B.Three-Dimensional CrackRecognition by Unsupervised Machine Learning[J].Rock Mechanics and Rock Engineering,2021:893-903.
[14]LI H F,SONG D Z,LIUY,et al.Automatic Pavement Crack Detection by Multi-Scale Image Fusion[J].IEEE Trans.Intelligent Transportation Systems,2019,20(6).
[15]ZHANG H C,QIAN Z D,TAN Y F,et al.Investigation of pavement crack detection based on deep learning method using weakly supervised instance segmentation framework[J].Construction and Building Materials,2022,358.
[16]ZHAO C Y,ZHANG H,LIAO D,et al.Rail surface defect detection model based on attention mechanism and mixed supervised learning[J].Computer Science,2022,49(S2):488-493.
[17]TENG S,LIU Z,CHEN G,et al.Concrete Crack Detection Basedon Well-Known Feature Extractor Model and the YOLO_v2 Network[J].Applied Sciences,2021,11(2):813.
[18] DU Y C,PAN N,XU Z H,et al.Pavement distress detection and classification based on YOLO network[J].International Journal of Pavement Engineering,2020(1):1-14.
[19]ZHU X,ZHU M,REN H.Method of plant leaf recognitionbased on improved deep convolutional neural network[J].Cognitive Systems Research,2018,52(DEC.):223-233.
[20]WANG M,WANG K,LI S,et al.Pill detection algorithm based on improved EfficienDet[J].Electronic Measurement Technology,2022,45(19):136-142.
[21]REDMON J,DIVVALA S,GIRSHICKR,et al.You Only Look Once:Unified,Real-Time Object Detection[C]// Computer Vision & Pattern Recognition.IEEE,2016.
[22]TAN M X,LE Q V.EfficientNet:Rethinking Model Scaling for Convolutional Neural Networks[J].arXiv:1905.11946,2019.
[23]ZHANG S,LIU Z,CHEN Y,et al.Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis[C]//ISA Transactions.2022.
[1] XU Jinlong, GUI Zhonghua, LI Jia'nan, LI Yingying, HAN Lin. FP8 Quantization and Inference Memory Optimization Based on MLIR [J]. Computer Science, 2024, 51(9): 112-120.
[2] HU Pengfei, WANG Youguo, ZHAI Qiqing, YAN Jun, BAI Quan. Night Vehicle Detection Algorithm Based on YOLOv5s and Bistable Stochastic Resonance [J]. Computer Science, 2024, 51(9): 173-181.
[3] LI Yuanxin, GUO Zhongfeng, YANG Junlin. Container Lock Hole Recognition Algorithm Based on Lightweight YOLOv5s [J]. Computer Science, 2024, 51(6A): 230900021-6.
[4] ZHAO Junjie, ZHOU Xiaojing, LI Jiaxing. Improved YOLOV7 for Fall Detection [J]. Computer Science, 2024, 51(6A): 230800039-6.
[5] ZHANG Yang, XIA Ying. Object Detection Method with Multi-scale Feature Fusion for Remote Sensing Images [J]. Computer Science, 2024, 51(3): 165-173.
[6] CHEN Dong, ZHOU Hao, YUAN Guowu, YANG Lingyu, CHENG Qiuyan, REN Ying, MA Yi. Mountain Fire Detection Algorithm of Transmission Line Based on Multi-scale Features and Coordinate Information [J]. Computer Science, 2024, 51(11A): 230900155-7.
[7] LIU Yunqing, WU Yue, ZHANG Qiong, YAN Fei, CHEN Shanshan. Road Crack Detection Based on Separable Convolution and Wave Transform Fusion [J]. Computer Science, 2024, 51(11A): 240100141-9.
[8] YU Yongbo, SUN Zhen, ZHU Lingxi, LI Qingdang. Improved YOLOv5s Algorithm for Detecting Fireworks in Urban Building Environments [J]. Computer Science, 2024, 51(11A): 240100051-7.
[9] CHEN Haiyan, MAO Lihong. Improved Lightweight Aerial Photography Object Detection Model Based on YOLOv5s [J]. Computer Science, 2024, 51(11A): 231100119-8.
[10] ZHAO Weidong, LU Ming, ZHANG Rui. Study on Road Crack Detection Based on Weakly Supervised Semantic Segmentation [J]. Computer Science, 2024, 51(11): 148-156.
[11] XIA Shufang, YUAN Bin, QU Zhong. Crack Detection of Concrete Pavement Based on Attention Mechanism and Deep Feature Optimization [J]. Computer Science, 2024, 51(11): 198-204.
[12] LONG Tao, DONG Anguo, LIU Laijun. Pavement Crack Detection Based on Attention Mechanism and Deformable Convolution [J]. Computer Science, 2023, 50(6A): 220300214-6.
[13] XIE Puxuan, CUI Jinrong, ZHAO Min. Electiric Bike Helment Wearing Detection Alogrithm Based on Improved YOLOv5 [J]. Computer Science, 2023, 50(6A): 220500005-6.
[14] QU Zhong, WANG Caiyun. Crack Detection of Concrete Pavement Based on Attention Mechanism and Lightweight DilatedConvolution [J]. Computer Science, 2023, 50(2): 231-236.
[15] LU Qi, YU Yuanqiang, XU Daoming, ZHANG Qi. Improved YOLOv5 Small Drones Target Detection Algorithm [J]. Computer Science, 2023, 50(11A): 220900050-8.
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.