{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T10:16:03Z","timestamp":1784715363387,"version":"3.55.0"},"reference-count":58,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61906143"],"award-info":[{"award-number":["61906143"]}]},{"name":"National Natural Science Foundation of China","award":["2021T140529"],"award-info":[{"award-number":["2021T140529"]}]},{"name":"National Natural Science Foundation of China","award":["2018M643584"],"award-info":[{"award-number":["2018M643584"]}]},{"name":"National Natural Science Foundation of China","award":["XJS211504"],"award-info":[{"award-number":["XJS211504"]}]},{"name":"National Natural Science Foundation of China","award":["2020JQ-305"],"award-info":[{"award-number":["2020JQ-305"]}]},{"name":"China Post-Doctoral Science Foundation","award":["61906143"],"award-info":[{"award-number":["61906143"]}]},{"name":"China Post-Doctoral Science Foundation","award":["2021T140529"],"award-info":[{"award-number":["2021T140529"]}]},{"name":"China Post-Doctoral Science Foundation","award":["2018M643584"],"award-info":[{"award-number":["2018M643584"]}]},{"name":"China Post-Doctoral Science Foundation","award":["XJS211504"],"award-info":[{"award-number":["XJS211504"]}]},{"name":"China Post-Doctoral Science Foundation","award":["2020JQ-305"],"award-info":[{"award-number":["2020JQ-305"]}]},{"name":"Fundamental Research Fund for the Central Universities","award":["61906143"],"award-info":[{"award-number":["61906143"]}]},{"name":"Fundamental Research Fund for the Central Universities","award":["2021T140529"],"award-info":[{"award-number":["2021T140529"]}]},{"name":"Fundamental Research Fund for the Central Universities","award":["2018M643584"],"award-info":[{"award-number":["2018M643584"]}]},{"name":"Fundamental Research Fund for the Central Universities","award":["XJS211504"],"award-info":[{"award-number":["XJS211504"]}]},{"name":"Fundamental Research Fund for the Central Universities","award":["2020JQ-305"],"award-info":[{"award-number":["2020JQ-305"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["61906143"],"award-info":[{"award-number":["61906143"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2021T140529"],"award-info":[{"award-number":["2021T140529"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2018M643584"],"award-info":[{"award-number":["2018M643584"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["XJS211504"],"award-info":[{"award-number":["XJS211504"]}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2020JQ-305"],"award-info":[{"award-number":["2020JQ-305"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In industrial production, flaws and defects inevitably appear on surfaces, resulting in unqualified products. Therefore, surface defect detection plays a key role in ensuring industrial product quality and maintaining industrial production lines. However, surface defects on different products have different manifestations, so it is difficult to regard all defective products as being within one category that has common characteristics. Defective products are also often rare in industrial production, making it difficult to collect enough samples. Therefore, it is appropriate to view the surface defect detection problem as a semi-supervised anomaly detection problem. In this paper, we propose an anomaly detection method that is based on dual attention and consistency loss to accomplish the task of surface defect detection. At the reconstruction stage, we employed both channel attention and pixel attention so that the network could learn more robust normal image reconstruction, which could in turn help to separate images of defects from defect-free images. Moreover, we proposed a consistency loss function that could exploit the differences between the multiple modalities of the images to improve the performance of the anomaly detection. Our experimental results showed that the proposed method could achieve a superior performance compared to the existing anomaly detection-based methods using the Magnetic Tile and MVTec AD datasets.<\/jats:p>","DOI":"10.3390\/s22145141","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"5141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Dual Attention-Based Industrial Surface Defect Detection with Consistency Loss"],"prefix":"10.3390","volume":"22","author":[{"given":"Xuyang","family":"Li","sequence":"first","affiliation":[{"name":"School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2679","DOI":"10.1109\/TIM.2018.2868490","article-title":"Deep architecture for high-speed railway insulator surface defect detection: Denoising autoencoder with multitask learning","volume":"68","author":"Kang","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/TIM.2018.2853958","article-title":"A coarse-to-fine model for rail surface defect detection","volume":"68","author":"Yu","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, X., Lv, J., Fang, Y., and Du, S. (2022). Online Detection of Surface Defects Based on Improved YOLOV3. Sensors, 22.","DOI":"10.3390\/s22030817"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, C., and Xiao, Z. (2021). Lychee surface defect detection based on deep convolutional neural networks with gan-based data augmentation. Agronomy, 11.","DOI":"10.3390\/agronomy11081500"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, T., Zheng, B., Zhang, J., Yi, C., Jiang, Y., Shui, Q., and Jian, H. (2021, January 12\u201314). Mango Surface Defect Detection Based on HALCON. Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference, Chongqing, China.","DOI":"10.1109\/IAEAC50856.2021.9390783"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ding, F., Yang, G., Ding, D., and Cheng, G. (2020, January 4\u20138). Retinal Nerve Fiber Layer Defect Detection with Position Guidance. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59722-1_72"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"044003","DOI":"10.1117\/1.JMI.5.4.044003","article-title":"Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma","volume":"5","author":"Panda","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wandt, B., and Rosenhahn, B. (2021, January 5\u20139). Same same but differnet: Semi-supervised defect detection with normalizing flows. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3161","DOI":"10.1109\/TIE.2021.3070507","article-title":"BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection","volume":"69","author":"Su","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.ins.2020.05.050","article-title":"D4Net: De-deformation defect detection network for non-rigid products with large patterns","volume":"547","author":"Xu","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alvarenga, T.A., Carvalho, A.L., Honorio, L.M., Cerqueira, A.S., Filho, L.M., and Nobrega, R.A. (2021). Detection and classification system for rail surface defects based on Eddy current. Sensors, 21.","DOI":"10.3390\/s21237937"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ho, C.C., Chou, W.C., and Su, E. (2021). Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection. Sensors, 21.","DOI":"10.3390\/s21217074"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5363750","DOI":"10.1155\/2021\/5363750","article-title":"Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning","volume":"2021","author":"Bakhshi","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_14","unstructured":"Qu, Y., Uddin, M.P., Gan, C., Xiang, Y., Gao, L., and Yearwood, J. (August, January 15). Blockchain-Enabled Federated Learning: A Survey. Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), Beijing, China."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/MNET.100.2000338","article-title":"Enabling Machine Learning with Service Function Chaining for Security Enhancement at 5G Edges","volume":"35","author":"Feng","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1109\/TCC.2019.2961093","article-title":"Efficient provision of service function chains in overlay networks using reinforcement learning","volume":"10","author":"Li","year":"2019","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/MNET.2019.1800426","article-title":"Enabling efficient service function chains at terrestrial-satellite hybrid cloud networks","volume":"33","author":"Feng","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3769","DOI":"10.1109\/TCYB.2020.3013416","article-title":"Learning latent representation for iot anomaly detection","volume":"52","author":"Vu","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bhatia, R., Benno, S., Esteban, J., Lakshman, T., and Grogan, J. (2019, January 9). Unsupervised machine learning for network-centric anomaly detection in IoT. Proceedings of the 3rd Acm Conext Workshop on Big Data, Machine Learning and Artificial Intelligence for Data Communication Networks, Orlando, FL, USA.","DOI":"10.1145\/3359992.3366641"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Feng, B., Tian, A., Yu, S., Li, J., Zhou, H., and Zhang, H. (2022). Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2022.3163776"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., and Gao, S. (2018, January 18\u201322). Future frame prediction for anomaly detection\u2013a new baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., and Shah, M. (2018, January 18\u201322). Real-world anomaly detection in surveillance videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref_23","unstructured":"Chalapathy, R., Menon, A.K., and Chawla, S. (2018). Anomaly detection using one-class neural networks. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ndteint.2016.04.006","article-title":"Defect detection in magnetic tile images based on stationary wavelet transform","volume":"83","author":"Yang","year":"2016","journal-title":"Ndt E Int."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ahn, E., Kumar, A., Feng, D., Fulham, M., and Kim, J. (2019, January 8\u201311). Unsupervised deep transfer feature learning for medical image classification. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging, Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759275"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, H., and Boulanger, P. (2022). Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features. Sensors, 22.","DOI":"10.3390\/s22072467"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, H., Liu, H., Gao, F., and Chen, Z. (2020, January 12\u201314). Adgan: A scalable gan-based architecture for image anomaly detection. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, China.","DOI":"10.1109\/ITNEC48623.2020.9085163"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Akcay, S., Atapour-Abarghouei, A., and Breckon, T.P. (2018, January 2\u20136). Ganomaly: Semi-supervised anomaly detection via adversarial training. Proceedings of the Asian Conference on Computer Vision, Perth, WA, Australia.","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., and Langs, G. (2017, January 25\u201330). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. Proceedings of the International Conference on Information Processing in Medical Imaging, Boone, NC, USA.","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref_30","unstructured":"Soukup, D., and Pinetz, T. (2018, January 15\u201316). Reliably Decoding Autoencoders\u2019 Latent Spaces for One-Class Learning Image Inspection Scenarios. Proceedings of the OAGM Workshop, Hall\/Tyrol, Austria."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1109\/TIM.2018.2803830","article-title":"Automatic visual detection system of railway surface defects with curvature filter and improved Gaussian mixture model","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/TPAMI.2012.147","article-title":"Visual saliency based on scale-space analysis in the frequency domain","volume":"35","author":"Li","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1016\/j.patcog.2009.12.005","article-title":"Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images","volume":"43","author":"Chao","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zenati, H., Romain, M., Foo, C.S., Lecouat, B., and Chandrasekhar, V. (2018, January 17\u201320). Adversarially learned anomaly detection. Proceedings of the 2018 IEEE International Conference on Data Mining, Singapore.","DOI":"10.1109\/ICDM.2018.00088"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, J., Yi, G., Zhang, S., and Wang, Y. (2021). An Unsupervised Generative Adversarial Network-Based Method for Defect Inspection of Texture Surfaces. Appl. Sci., 11.","DOI":"10.3390\/app11010283"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Choi, J., and Kim, C. (October, January 30). Unsupervised detection of surface defects: A two-step approach. Proceedings of the 2012 19th IEEE International Conference on Image Processing, Orlando, FL, USA.","DOI":"10.1109\/ICIP.2012.6467040"},{"key":"ref_37","unstructured":"Kim, M.S., Park, T., and Park, P. (2019, January 9\u201312). Classification of steel surface defect using convolutional neural network with few images. Proceedings of the 2019 12th Asian Control Conference, Kitakyushu, Japan."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C. (2020, January 4\u201319). Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Y., Zheng, L., Yin, L., Chen, J., and Lu, J. (2021). Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images. Sensors, 21.","DOI":"10.3390\/s21206773"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Youkachen, S., Ruchanurucks, M., Phatrapomnant, T., and Kaneko, H. (2019, January 25\u201327). Defect segmentation of hot-rolled steel strip surface by using convolutional auto-encoder and conventional image processing. Proceedings of the 2019 10th International Conference of Information and Communication Technology for Embedded Systems, Bangkok, Thailand.","DOI":"10.1109\/ICTEmSys.2019.8695928"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bergmann, P., L\u00f6we, S., Fauser, M., Sattlegger, D., and Steger, C. (2018). Improving unsupervised defect segmentation by applying structural similarity to autoencoders. arXiv.","DOI":"10.5220\/0007364503720380"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","article-title":"Deep learning for anomaly detection: A review","volume":"54","author":"Pang","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_43","unstructured":"Nakanishi, M., Sato, K., and Terada, H. (2021). Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","article-title":"f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks","volume":"54","author":"Schlegl","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ak\u00e7ay, S., Atapour-Abarghouei, A., and Breckon, T.P. (2019, January 14\u201319). Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. Proceedings of the 2019 International Joint Conference on Neural Networks, Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851808"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tang, T.W., Kuo, W.H., Lan, J.H., Ding, C.F., Hsu, H., and Young, H.T. (2020). Anomaly detection neural network with dual auto-encoders GAN and its industrial inspection applications. Sensors, 20.","DOI":"10.3390\/s20123336"},{"key":"ref_48","unstructured":"Berthelot, D., Schumm, T., and Metz, L. (2017). Began: Boundary equilibrium generative adversarial networks. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Carrara, F., Amato, G., Brombin, L., Falchi, F., and Gennaro, C. (2021, January 10\u201315). Combining gans and autoencoders for efficient anomaly detection. Proceedings of the 2020 25th International Conference on Pattern Recognition, Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412253"},{"key":"ref_50","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., and Darrell, T. (2016). Adversarial feature learning. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhao, H., Kong, X., He, J., Qiao, Y., and Dong, C. (2020, January 23\u201328). Efficient image super-resolution using pixel attention. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-67070-2_3"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021, January 3\u20138). Attentional feature fusion. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"ref_53","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, QC, Canada."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1007\/s11263-020-01400-4","article-title":"The MVTec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection","volume":"129","author":"Bergmann","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s00371-018-1588-5","article-title":"Surface defect saliency of magnetic tile","volume":"36","author":"Huang","year":"2020","journal-title":"Vis. Comput."},{"key":"ref_56","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020, January 7\u201312). Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_57","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_58","unstructured":"Ling, C.X., Huang, J., and Zhang, H. (2003, January 9\u201315). AUC: A statistically consistent and more discriminating measure than accuracy. Proceedings of the International Joint Conference on Artificial Intelligence, Acapulco, Mexico."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:46:46Z","timestamp":1760140006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,8]]},"references-count":58,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145141"],"URL":"https:\/\/doi.org\/10.3390\/s22145141","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,8]]}}}