{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:02:35Z","timestamp":1771048955921,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Automated anomaly detection in transportation infrastructure is essential for enhancing safety and reducing the operational costs associated with manual inspection protocols. This study presents an improved neuromorphic vision system, which extends the prior SIFT-SNN (scale-invariant feature transform\u2013spiking neural network) proof-of-concept by incorporating temporal feature aggregation for context-aware and sequence-stable detection. Analysis of classical stitching-based pipelines exposed sensitivity to motion and lighting variations, motivating the proposed temporally smoothed neuromorphic design. SIFT keypoints are encoded into latency-based spike trains and classified using a leaky integrate-and-fire (LIF) spiking neural network implemented in PyTorch. Evaluated across three hardware configurations\u2014an NVIDIA RTX 4060 GPU, an Intel i7 CPU, and a simulated Jetson Nano\u2014the system achieved 92.3% accuracy and a macro F1 score of 91.0% under five-fold cross-validation. Inference latencies were measured at 9.5 ms, 26.1 ms, and ~48.3 ms per frame, respectively. Memory footprints were under 290 MB, and power consumption was estimated to be between 5 and 65 W. The classifier distinguishes between safe, partially dislodged, and fully dislodged barrier pins, which are critical failure modes for the Auckland Harbour Bridge\u2019s Movable Concrete Barrier (MCB) system. Temporal smoothing further improves recall for ambiguous cases. By achieving a compact model size (2.9 MB), low-latency inference, and minimal power demands, the proposed framework offers a deployable, interpretable, and energy-efficient alternative to conventional CNN-based inspection tools. Future work will focus on exploring the generalisability and transferability of the work presented, additional input sources, and human\u2013computer interaction paradigms for various deployment infrastructures and advancements.<\/jats:p>","DOI":"10.3390\/jimaging12020064","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T16:12:37Z","timestamp":1770048757000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SIFT-SNN for Traffic-Flow Infrastructure Safety: A Real-Time Context-Aware Anomaly Detection Framework"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7367-4984","authenticated-orcid":false,"given":"Munish","family":"Rathee","sequence":"first","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-4322","authenticated-orcid":false,"given":"Boris","family":"Ba\u010di\u0107","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4953-0662","authenticated-orcid":false,"given":"Maryam","family":"Doborjeh","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand"},{"name":"Knowledge Engineering and Discovery Research Innovation, Auckland University of Technology, Auckland 1010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"ref_1","unstructured":"Centers for Disease Control and Prevention (CDC) (2025, June 15). Global Road Safety, Available online: https:\/\/www.cdc.gov\/transportation-safety\/global."},{"key":"ref_2","unstructured":"Global Road Safety Facility (2025, June 15). Global Road Safety Facility Annual Report 2023. Available online: https:\/\/documents1.worldbank.org\/curated\/en\/099634305072412983\/pdf\/IDU14bccf9fb16f8914d9f1a3861ebb97d26441e.pdf."},{"key":"ref_3","unstructured":"World Bank (2025, June 15). Roads, Total Network (km)\u2014World Development Indicators. Available online: https:\/\/databank.worldbank.org\/metadataglossary\/world-development-indicators\/series\/IS.ROD.TOTL.KM."},{"key":"ref_4","unstructured":"World Health Organization (2025, June 15). Global Status Report on Road Safety: Time for Action. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789241563840."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e390","DOI":"10.1016\/S2542-5196(19)30170-6","article-title":"The global macroeconomic burden of road injuries: Estimates and projections for 166 countries","volume":"3","author":"Chen","year":"2019","journal-title":"Lancet Planet. Health"},{"key":"ref_6","unstructured":"United Nations Economic Commission for Europe (UNECE) (2025, June 29). Statistics of Road Traffic Accidents in Europe and North America\u20142023 Edition. Available online: https:\/\/unece.org\/transport\/publications\/2023-statistics-road-traffic-accidents."},{"key":"ref_7","unstructured":"Reuters (2025, June 15). Death Toll in Gujarat Bridge Collapse Rises to 132. Available online: https:\/\/www.reuters.com\/graphics\/INDIA-ACCIDENT\/BRIDGE\/akpeqgjawpr\/."},{"key":"ref_8","unstructured":"News, B. (2025, June 15). Genoa Bridge Collapse: What We Know. Available online: https:\/\/www.bbc.com\/news\/world-europe-45193452."},{"key":"ref_9","unstructured":"Administration, F.H. (2025, June 15). Bridge Inspection Program, Available online: https:\/\/www.fhwa.dot.gov\/bridge\/inspection\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s42524-024-3128-5","article-title":"Artificial intelligence in infrastructure construction: A critical review","volume":"12","author":"Chen","year":"2024","journal-title":"Front. Eng. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rathee, M., Ba\u010di\u0107, B., and Doborjeh, M. (2023). Automated road defect and anomaly detection for traffic safety: A systematic review. Sensors, 23.","DOI":"10.3390\/s23125656"},{"key":"ref_12","unstructured":"New Zealand Transport Agency Waka Kotahi (2025, April 10). Auckland Harbour Bridge Factsheet, Available online: https:\/\/www.nzta.govt.nz\/assets\/site-resources\/content\/about\/docs\/auckland-harbour-bridge-factsheet.pdf."},{"key":"ref_13","unstructured":"New Zealand Transport Agency Waka Kotahi (2025, April 10). How to Move a Concrete Motorway Barrier, Available online: https:\/\/www.nzta.govt.nz\/media-releases\/how-to-move-a-concrete-motorway-barrier\/."},{"key":"ref_14","unstructured":"Cottrell, B.H. (2026, January 02). Evaluation of a Movable Concrete Barrier System, Available online: https:\/\/rosap.ntl.bts.gov\/view\/dot\/19352."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, M., Wang, X., Wang, H., and Yang, M. (2025). LTGS-Net: Local Temporal and Global Spatial Network for Weakly Supervised Video Anomaly Detection. Sensors, 25.","DOI":"10.3390\/s25164884"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sooksatra, S., and Watcharapinchai, S. (2022). A comprehensive review on temporal-action proposal generation. J. Imaging, 8.","DOI":"10.3390\/jimaging8080207"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shahsavarani, S., Lopez, F., Ibarra-Castanedo, C., and Maldague, X.P. (2024). Advanced image stitching method for dual-sensor inspection. Sensors, 24.","DOI":"10.3390\/s24123778"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, Z., Zhang, X., Sui, T., and Li, M. (2022). A very fast image stitching algorithm for PET bottle caps. J. Imaging, 8.","DOI":"10.3390\/jimaging8100275"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rathee, M., Ba\u010di\u0107, B., and Doborjeh, M. (2025). Hybrid SIFT-SNN for Efficient Anomaly Detection of Traffic Flow-Control Infrastructure. arXiv.","DOI":"10.1109\/IVCNZ67716.2025.11281836"},{"key":"ref_20","first-page":"103","article-title":"On-road object detection and tracking based on radar and vision fusion: A review","volume":"14","author":"Tang","year":"2021","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, F., Zhang, Y.D., and Zhu, Y.J. (2016, January 25\u201328). Road crack detection using deep convolutional neural network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533052"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1111\/mice.12387","article-title":"Road damage detection and classification using deep neural networks with smartphone images","volume":"33","author":"Maeda","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1111\/mice.12141","article-title":"Vision-based automated crack detection for bridge inspection","volume":"30","author":"Yeum","year":"2015","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_24","first-page":"100031","article-title":"Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey","volume":"2","author":"Zhou","year":"2023","journal-title":"J. Infrastruct. Intell. Resil."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., and Gross, H.-M. (2017, January 14\u201319). How to get pavement distress detection ready for deep learning? A systematic approach. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966101"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020, January 13\u201319). BDD100K: A diverse driving dataset for heterogeneous multitask learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/TITS.2015.2477675","article-title":"Automatic crack detection on two-dimensional pavement images: An algorithm based on minimal path selection","volume":"17","author":"Amhaz","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108210","DOI":"10.1016\/j.ijfatigue.2024.108210","article-title":"Probabilistic corrosion-fatigue prognosis of rib-to-deck welded joints in coastal weathering steel bridges exposed to heavy traffics","volume":"182","author":"Zhu","year":"2024","journal-title":"Int. J. Fatigue"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105767","DOI":"10.1016\/j.autcon.2024.105767","article-title":"Recent advances on inspection, monitoring, and assessment of bridge cables","volume":"168","author":"Kong","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Graves, W., Aminfar, K., and Lattanzi, D. (2022). Full-scale highway bridge deformation tracking via photogrammetry and remote sensing. Remote Sens., 14.","DOI":"10.3390\/rs14122767"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Luo, K., Kong, X., Zhang, J., Hu, J., Li, J., and Tang, H. (2023). Computer vision-based bridge inspection and monitoring: A review. Sensors, 23.","DOI":"10.3390\/s23187863"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kim, I.B., Cho, J.S., Zi, G.S., Cho, B.S., Lee, S.M., and Kim, H.U. (2021). Detection and identification of expansion joint gap of road bridges by Machine Learning using line-scan camera images. Appl. Syst. Innov., 4.","DOI":"10.3390\/asi4040094"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rathee, M., Ba\u010di\u0107, B., and Doborjeh, M. (2024). Hybrid machine learning for automated road safety inspection of Auckland Harbour Bridge. Electronics, 13.","DOI":"10.3390\/electronics13153030"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Luo, H., Cai, L., and Li, C. (2023). Rail surface defect detection based on an improved YOLOv5s. Appl. Sci., 13.","DOI":"10.3390\/app13127330"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"539","DOI":"10.3390\/signals4030029","article-title":"Computer Vision and Image Processing in structural health monitoring: Overview of recent applications","volume":"4","author":"Ferraris","year":"2023","journal-title":"Signals"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kralovec, C., and Schagerl, M. (2020). Review of structural health monitoring methods regarding a multi-sensor approach for damage assessment of metal and composite structures. Sensors, 20.","DOI":"10.3390\/s20030826"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ba\u010di\u0107, B., Rathee, M., and Pears, R. (2020). Automating inspection of Moveable Lane Barrier for Auckland Harbour Bridge traffic safety. Proceedings of the Neural Information Processing, Bangkok, Thailand, 23\u201327 November 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-63830-6_13"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lee, H., and Oh, J. (2023). 3D displacement measurement of railway bridge according to cyclic loads of different types of railcars with sequential photogrammetry. Appl. Sci., 13.","DOI":"10.3390\/app13031359"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s41109-021-00438-8","article-title":"A computational framework for modeling complex sensor network data using graph signal processing and graph neural networks in structural health monitoring","volume":"6","author":"Bloemheuvel","year":"2021","journal-title":"Appl. Netw. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bhavanasi, G., Neven, D., Arteaga, M., Ditzel, S., Dehaeck, S., and Bey-Temsamani, A. (2025). Enhanced vision-based quality inspection: A multiview artificial intelligence framework for defect detection. Sensors, 25.","DOI":"10.3390\/s25061703"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","article-title":"Networks of spiking neurons: The third generation of neural network models","volume":"10","author":"Maass","year":"1997","journal-title":"Neural Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/s41586-019-1677-2","article-title":"Towards spike-based machine intelligence with neuromorphic computing","volume":"575","author":"Roy","year":"2019","journal-title":"Nature"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gerstner, W., and Kistler, W.M. (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity, Cambridge University Press.","DOI":"10.1017\/CBO9780511815706"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pfeiffer, M., and Pfeil, T. (2018). Deep learning with spiking neurons: Opportunities and challenges. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00774"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MM.2018.112130359","article-title":"Loihi: A neuromorphic manycore processor with on-chip Learning","volume":"38","author":"Davies","year":"2018","journal-title":"IEEE Micro."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1109\/JSEN.2021.3120845","article-title":"A spiking neural network with spike-timing-dependent plasticity for surface roughness analysis","volume":"22","author":"Jiang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vemuru, K.V. (2020). Image edge detector with Gabor type filters using a Spiking Neural Network of biologically inspired neurons. Algorithms, 13.","DOI":"10.3390\/a13070165"},{"key":"ref_48","first-page":"136782","article-title":"Autonomous driving with spiking neural networks","volume":"37","author":"Zhu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Duong, H.-T., Le, V.-T., and Hoang, V.T. (2023). Deep learning-based anomaly detection in video surveillance: A survey. Sensors, 23.","DOI":"10.3390\/s23115024"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3D convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_51","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., and He, K. (November, January 27). Slowfast networks for video recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"21162","DOI":"10.1038\/s41598-023-48432-7","article-title":"LMFD: Lightweight multi-feature descriptors for image stitching","volume":"13","author":"Fan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3724\/SP.J.2096-5796.2018.0008","article-title":"A survey on image and video stitching","volume":"1","author":"Wei","year":"2019","journal-title":"Virtual Real. Intell. Hardw."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.patrec.2019.06.018","article-title":"GPU based parallel optimization for real time panoramic video stitching","volume":"133","author":"Du","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1109\/TPAMI.2016.2574707","article-title":"HOTS: A hierarchy of event-based time-surfaces for pattern recognition","volume":"39","author":"Lagorce","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/B978-0-12-336156-1.50061-6","article-title":"Contrast limited adaptive histogram equalization","volume":"4","author":"Zuiderveld","year":"1994","journal-title":"Graph. Gems"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Turker, K., Sharf, I., and Trentini, M. (2012, January 14\u201318). Step negotiation with wheel traction: A strategy for a wheel-legged robot. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, St. Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224645"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/64\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:10:32Z","timestamp":1771045832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/2\/64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,31]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jimaging12020064"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12020064","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,31]]}}}