Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)

Automation in Construction - Tập 155 - Trang 105062 - 2023
Jiale Li1, Chenglong Yuan1, Xuefei Wang1
1School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China

Tài liệu tham khảo

Zhang, 2022, Road damage detection using UAV images based on multi-level attention mechanism, Autom. Constr., 144, 1, 10.1016/j.autcon.2022.104613 Park, 2019, Patch-based crack detection in black box images using convolutional neural networks, J. Comput. Civ. Eng., 33, 1, 10.1061/(ASCE)CP.1943-5487.0000831 Ji, 2020, An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement, Autom. Constr., 114, 1, 10.1016/j.autcon.2020.103176 Chu, 2022, A review on pavement distress and structural defects detection and quantification technologies using imaging approaches, J. Traff. Transport. Eng. (Engl. Ed.), 9, 135 Huyan, 2019, Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network, Autom. Constr., 107, 1, 10.1016/j.autcon.2019.102946 Mandal, 2018, Automated road crack detection using deep convolutional neural networks, 5212 Yang, 2022, Datasets and processing methods for boosting visual inspection of civil infrastructure: a comprehensive review and algorithm comparison for crack classification, segmentation, and detection, Constr. Build. Mater., 356, 1, 10.1016/j.conbuildmat.2022.129226 Wang, 2019, Pavement crack image acquisition methods and crack extraction algorithms: a review, J. Traff. Transport. Eng. (Engl. Ed.), 6, 535 Sun, 2023, Employing histogram of oriented gradient to enhance concrete crack detection performance with classification algorithm and Bayesian optimization, Eng. Fail. Anal., 150, 1, 10.1016/j.engfailanal.2023.107351 Shtayat, 2023, Using supervised machine learning algorithms in pavement degradation monitoring, In. J. Transport. Sci. Technol., 12, 628, 10.1016/j.ijtst.2022.10.001 Yang, 2023, Multi-scale triple-attention network for pixelwise crack segmentation, Autom. Constr., 150, 1, 10.1016/j.autcon.2023.104853 Asadi Shamsabadi, 2022, Vision transformer-based autonomous crack detection on asphalt and concrete surfaces, Autom. Constr., 140, 1, 10.1016/j.autcon.2022.104316 Liu, 2023, Learning position information from attention: end-to-end weakly supervised crack segmentation with GANs, Comput. Ind., 149, 1, 10.1016/j.compind.2023.103921 Wang, 2022, Automatic concrete crack segmentation model based on transformer, Autom. Constr., 139, 1, 10.1016/j.autcon.2022.104275 Zhou, 2023, Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance, Autom. Constr., 146, 1, 10.1016/j.autcon.2022.104678 Liu, 2021, Automated crack pattern recognition from images for condition assessment of concrete structures, Autom. Constr., 128, 1, 10.1016/j.autcon.2021.103765 Wang, 2022, Fully convolution network architecture for steel-beam crack detection in fast-stitching images, Mech. Syst. Signal Process., 165, 1, 10.1016/j.ymssp.2021.108377 Wei, 2021, Damage inspection for road markings based on images with hierarchical semantic segmentation strategy and dynamic homography estimation, Autom. Constr., 131, 1, 10.1016/j.autcon.2021.103876 Xiao, 2023, Pavement crack detection with hybrid-window attentive vision transformers, Int. J. Appl. Earth Obs. Geoinf., 116, 1 Xie, 2022, Efficient pavement distress detection based on attention fusion and feature integration, 374 Zeng, 2022, A computer vision-based method to identify the international roughness index of highway pavements, J. Infrastruct. Intell. Resilien., 1, 1 Liu, 2019, DeepCrack: a deep hierarchical feature learning architecture for crack segmentation, Neurocomputing, 338, 139, 10.1016/j.neucom.2019.01.036 Chen, 2020, Pavement crack detection and recognition using the architecture of segNet, J. Ind. Inf. Integr., 18, 1 Guo, 2023, Pavement crack detection based on transformer network, Autom. Constr., 145, 1, 10.1016/j.autcon.2022.104646 Du, 2023, Modeling automatic pavement crack object detection and pixel-level segmentation, Autom. Constr., 150, 1, 10.1016/j.autcon.2023.104840 Weng, 2023, Unsupervised domain adaptation for crack detection, Autom. Constr., 153, 1, 10.1016/j.autcon.2023.104939 Pietersen, 2022, Automated method for airfield pavement condition index evaluations, Autom. Constr., 141, 1, 10.1016/j.autcon.2022.104408 Zhong, 2022, Multi-scale feature fusion network for pixel-level pavement distress detection, Autom. Constr., 141, 1, 10.1016/j.autcon.2022.104436 Dong, 2022, Automatic damage segmentation in pavement videos by fusing similar feature extraction siamese network (SFE-SNet) and pavement damage segmentation capsule network (PDS-CapsNet), Autom. Constr., 143, 1, 10.1016/j.autcon.2022.104537 Mei, 2020, A cost effective solution for pavement crack inspection using cameras and deep neural networks, Constr. Build. Mater., 256, 1, 10.1016/j.conbuildmat.2020.119397 Li, 2021, Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field, Measurement, 170, 1, 10.1016/j.measurement.2020.108693 Qiu, 2023, Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images, Autom. Constr., 147, 1, 10.1016/j.autcon.2023.104745 Ren, 2023, YOLOv5s-M: a deep learning network model for road pavement damage detection from urban street-view imagery, Int. J. Appl. Earth Obs. Geoinf., 120, 1 Zhu, 2022, Pavement distress detection using convolutional neural networks with images captured via UAV, Autom. Constr., 133, 1, 10.1016/j.autcon.2021.103991 Bianchi, 2022, Visual structural inspection datasets, Autom. Constr., 139, 1, 10.1016/j.autcon.2022.104299 Hou, 2021, The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis, Engineering, 7, 845, 10.1016/j.eng.2020.07.030 Nappo, 2021, Use of UAV-based photogrammetry products for semi-automatic detection and classification of asphalt road damage in landslide-affected areas, Eng. Geol., 294, 1, 10.1016/j.enggeo.2021.106363 Sunkara, 2022, No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects, Preprint arXiv Redmon, 2016, You only look once: unified, real-time object detection, 779 Tan, 2019, EfficientDet: scalable and efficient object detection, 10778 Zheng, 2022, Enhancing geometric factors in model learning and inference for object detection and instance segmentation, IEEE Transact. Cybernet., 52, 8574, 10.1109/TCYB.2021.3095305 Zhou, 2019, Omni-scale feature learning for person re-identification, 3701 Chollet, 2016, Xception: deep learning with depthwise separable convolutions, Preprint arXiv, 1800 El Hakea, 2023, Recent computer vision applications for pavement distress and condition assessment, Autom. Constr., 146, 1, 10.1016/j.autcon.2022.104664 Arya, 2022, RDD2022: A multi-national image dataset for automatic road damage detection, Preprint arXiv Hawkins, 2003, Assessing model fit by cross-validation, J. Chem. Inf. Comput. Sci., 43, 579, 10.1021/ci025626i Bianchi, 2021, COCO-bridge: structural detail data set for bridge inspections, J. Comput. Civ. Eng., 35, 1, 10.1061/(ASCE)CP.1943-5487.0000949 Zhao, 2023, RDD-YOLO: a modified YOLO for detection of steel surface defects, Measurement, 214, 1, 10.1016/j.measurement.2023.112776 Zheng, 2015, Person re-identification meets image search, Preprint arXiv Kingma, 2014, Adam: a method for stochastic optimization, Preprint arXiv Loshchilov, 2017, Fixing weight decay regularization in Adam, Preprint arXiv Ruder, 2016, An overview of gradient descent optimization algorithms, Preprint arXiv Wang, 2023, YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 7464 Jia, 2023, Crack identification for marine engineering equipment based on improved SSD and YOLOv5, Ocean Eng., 268, 1, 10.1016/j.oceaneng.2022.113534 Guo, 2022, Road damage detection algorithm for improved YOLOv5, Sci. Rep., 12, 1, 10.1038/s41598-022-19674-8 Zhong, 2023, A deeper generative adversarial network for grooved cement concrete pavement crack detection, Eng. Appl. Artif. Intell., 119, 1, 10.1016/j.engappai.2022.105808 Sarmiento, 2021, Pavement distress detection and segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines, Preprint arXiv Zoph, 2017, Learning transferable architectures for scalable image recognition, 8697 Sandler, 2018, MobileNetV2: Inverted Residuals and Linear Bottlenecks, Preprint arXiv, 4510 Iandola, 2016, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size, Preprint arXiv Cui, 2022, Automatic recognition and tracking of highway layer-interface using faster R-CNN, J. Appl. Geophys., 196, 1, 10.1016/j.jappgeo.2021.104477 Hou, 2022, Vision image monitoring on transportation infrastructures: a lightweight transfer learning approach, IEEE Trans. Intell. Transp. Syst., 1, 10.1109/TITS.2022.3150536 Wan, 2023, A novel transformer model for surface damage detection and cognition of concrete bridges, Expert Syst. Appl., 213, 1, 10.1016/j.eswa.2022.119019 Maeda, 2018, Road damage detection and classification using deep neural networks with smartphone images, Comput.-Aided Civil Infrastruct. Eng., 33, 1127, 10.1111/mice.12387