{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T05:21:18Z","timestamp":1774675278696,"version":"3.50.1"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19145-4","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T04:01:45Z","timestamp":1714622505000},"page":"3809-3840","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems"],"prefix":"10.1007","volume":"84","author":[{"given":"Michael Abebe","family":"Berwo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0594-5877","authenticated-orcid":false,"given":"Nadeem","family":"Sarwar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jabar","family":"Mahmood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mansourah","family":"Aljohani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mostafa","family":"Elhosseini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,2]]},"reference":[{"key":"19145_CR1","first-page":"1","volume":"2021","author":"J Mahmood","year":"2021","unstructured":"Mahmood J, Duan Z, Yang Y, Wang Q, Nebhen J, Bhutta MNM (2021) Security in vehicular ad hoc networks: challenges and countermeasures. Secur Commun Netw 2021:1\u201320","journal-title":"Secur Commun Netw"},{"key":"19145_CR2","doi-asserted-by":"crossref","unstructured":"Vu TA, Pham LH, Huynh TK, Ha SVU (2017) Nighttime vehicle detection and classification via headlights trajectories matching. In: 2017 international conference on system science and engineering (ICSSE), pp. 221\u2013225. IEEE","DOI":"10.1109\/ICSSE.2017.8030869"},{"issue":"12","key":"19145_CR3","doi-asserted-by":"publisher","first-page":"10755","DOI":"10.1007\/s13369-020-04837-4","volume":"45","author":"A Kausar","year":"2020","unstructured":"Kausar A, Jamil A, Nida N, Yousaf MH (2020) Two-wheeled vehicle detection using two-step and single-step deep learning models. Arab J Sci Eng 45(12):10755\u201310773","journal-title":"Arab J Sci Eng"},{"key":"19145_CR4","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.compind.2018.03.014","volume":"98","author":"Edmund J Sadgrove","year":"2018","unstructured":"Sadgrove Edmund J, Falzon Greg, Miron David, Lamb David W (2018) Real-time object detection in agricultural\/remote environments using the multiple-expert colour feature extreme learning machine (mec-elm). Comput Ind 98:183\u2013191","journal-title":"Comput Ind"},{"key":"19145_CR5","unstructured":"Tong Z, Chen Y, Xu Z, Yu R (2023) Wise-IoU: Bounding box regression loss with dynamic focusing mechanism. arXiv preprint arXiv:2301.10051"},{"issue":"7","key":"19145_CR6","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1109\/TIP.2006.877062","volume":"15","author":"Z Sun","year":"2006","unstructured":"Sun Z, Bebis G, Miller R (2006) Monocular precrash vehicle detection: features and classifiers. IEEE Trans Image Process 15(7):2019\u20132034","journal-title":"IEEE Trans Image Process"},{"key":"19145_CR7","unstructured":"Mounika N (2016) Face detection using region descriptors"},{"key":"19145_CR8","doi-asserted-by":"crossref","unstructured":"Dalal, Navneet and Triggs, Bill (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), vol. 1, pp. 886\u2013893. Ieee","DOI":"10.1109\/CVPR.2005.177"},{"key":"19145_CR9","doi-asserted-by":"crossref","unstructured":"Zhang G, Huang X, Li SZ, Wang Y, Wu X (2004) Boosting local binary pattern (LBP)-based face recognition. In: Chinese Conference on Biometric Recognition, pp. 179\u2013186. Springer","DOI":"10.1007\/978-3-540-30548-4_21"},{"issue":"5","key":"19145_CR10","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.neunet.2006.12.003","volume":"20","author":"R Capparuccia","year":"2007","unstructured":"Capparuccia R, De Leone R, Marchitto E (2007) Integrating support vector machines and neural networks. Neural Netw 20(5):590\u2013597","journal-title":"Neural Netw"},{"issue":"2","key":"19145_CR11","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s12652-021-03332-4","volume":"14","author":"L Zhang","year":"2023","unstructured":"Zhang L, Wang J, An Z (2023) Vehicle recognition algorithm based on Haar-like features and improved Adaboost classifier. J Ambient Intell Humaniz Comput 14(2):807\u2013815","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"5","key":"19145_CR12","first-page":"3556","volume":"2","author":"MG Krishna","year":"2012","unstructured":"Krishna MG, Srinivasulu A (2012) Face detection system on Adaboost algorithm using Haar classifiers. Int J Mod Eng Res 2(5):3556\u20133560","journal-title":"Int J Mod Eng Res"},{"key":"19145_CR13","doi-asserted-by":"crossref","unstructured":"Joshi AJ, Porikli F (2010) Scene-adaptive human detection with incremental active learning. In: 2010 20th International Conference on Pattern Recognition, pp. 2760\u20132763. IEEE","DOI":"10.1109\/ICPR.2010.676"},{"key":"19145_CR14","doi-asserted-by":"crossref","unstructured":"Li X, Guo X (2013) A HOG feature and SVM based method for forward vehicle detection with single camera. In: 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, vo. 1, pp. 263\u2013266. IEEE","DOI":"10.1109\/IHMSC.2013.69"},{"issue":"4","key":"19145_CR15","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1007\/s10044-020-00874-9","volume":"23","author":"M Hassaballah","year":"2020","unstructured":"Hassaballah M, Kenk MA, El-Henawy IM (2020) Local binary pattern-based on-road vehicle detection in urban traffic scene. Pattern Anal Appl 23(4):1505\u20131521","journal-title":"Pattern Anal Appl"},{"key":"19145_CR16","doi-asserted-by":"crossref","unstructured":"Girshick, R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"19145_CR17","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"19145_CR18","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"19145_CR19","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ijleo.2019.02.038","volume":"183","author":"Z Yi","year":"2019","unstructured":"Yi Z, Yongliang S, Jun Z (2019) An improved tiny-yolov3 pedestrian detection algorithm. Optik 183:17\u201323","journal-title":"Optik"},{"key":"19145_CR20","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"19145_CR21","unstructured":"Jiang Z, Zhao L, Li S, Jia Y (2020) Real-time object detection method based on improved YOLOv4-tiny. arXiv preprint arXiv:2011.04244"},{"key":"19145_CR22","unstructured":"Jocher G: YOLOv5 by Ultralytics. https:\/\/github.com\/ultralytics\/yolov5,2020, Accessed June 24, 2023"},{"key":"19145_CR23","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"19145_CR24","unstructured":"Jocher G, Chaurasia A, Qiu J: YOLO by Ultralytics. https:\/\/github.com\/open-mmlab\/mmyolo\/tree\/main\/configs\/yolov8,2023 Accessed June 24, 2023"},{"key":"19145_CR25","unstructured":"race: Soaring Eagle Edge of Summer MX - ATV Episode - 2015. https:\/\/youtu.be\/ZyE3t3lG-vU. Accessed February 24, 2022"},{"key":"19145_CR26","unstructured":"bicyclerace: Bretagne Classic - Ouest-France (1.UWT). https:\/\/youtu.be\/MU-HhNW44z0, Accessed February 24, 2022"},{"key":"19145_CR27","unstructured":"Kenk MA, Hassaballah M (2020) DAWN: vehicle detection in adverse weather nature dataset. arXiv preprint arXiv:2008.05402"},{"key":"19145_CR28","doi-asserted-by":"publisher","first-page":"102907","DOI":"10.1016\/j.cviu.2020.102907","volume":"193","author":"L Wen","year":"2020","unstructured":"Wen L, Du D, Cai Z, Lei Z, Chang MC, Qi H, Lim J, Yang MH, Lyu S (2020) UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Comput Vis Image Underst 193:102907","journal-title":"Comput Vis Image Underst"},{"key":"19145_CR29","doi-asserted-by":"crossref","unstructured":"Marathe A, Ramanan D, Walambe R, Kotecha K (2023) WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3317\u20133326","DOI":"10.1109\/CVPRW59228.2023.00334"},{"issue":"12","key":"19145_CR30","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/83.806630","volume":"8","author":"T Chen","year":"1999","unstructured":"Chen T, Ma KK, Chen LH (1999) Tri-state median filter for image denoising. IEEE Trans Image Process 8(12):1834\u20131838","journal-title":"IEEE Trans Image Process"},{"key":"19145_CR31","doi-asserted-by":"crossref","unstructured":"Bovik Alan C, Jr Munson, David C (1986) Edge detection using median comparisons. Comput Vis Graph Image Process 33(3):377\u2013389","DOI":"10.1016\/0734-189X(86)90184-2"},{"key":"19145_CR32","unstructured":"Sen-Ching SC, Kamath C (2004) Robust techniques for background subtraction in urban traffic video. In: Visual Communications and Image Processing 2004, vol. 5308, pp. 881\u2013892. International Society for Optics and Photonics"},{"issue":"6","key":"19145_CR33","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1109\/TASSP.1984.1164468","volume":"32","author":"JP Fitch","year":"1984","unstructured":"Fitch JP, Coyle E, Gallagher N (1984) Median filtering by threshold decomposition. IEEE Trans Acoust Speech Signal Process 32(6):1183\u20131188","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"issue":"5","key":"19145_CR34","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/TIP.2017.2781375","volume":"27","author":"O Green","year":"2017","unstructured":"Green O (2017) Efficient scalable median filtering using histogram-based operations. IEEE Trans Image Process 27(5):2217\u20132228","journal-title":"IEEE Trans Image Process"},{"key":"19145_CR35","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI conference on artificial intelligence 34:12993\u201313000","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"19145_CR36","doi-asserted-by":"publisher","first-page":"105686","DOI":"10.1109\/ACCESS.2021.3100414","volume":"9","author":"X Wang","year":"2021","unstructured":"Wang X, Song J (2021) ICIoU: Improved loss based on complete intersection over union for bounding box regression. IEEE Access 9:105686\u2013105695","journal-title":"IEEE Access"},{"key":"19145_CR37","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: More features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"issue":"4","key":"19145_CR38","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3390\/jlpea12040051","volume":"12","author":"N Ravi","year":"2022","unstructured":"Ravi N, Naqvi S, El-Sharkawy M (2022) Biou: An improved bounding box regression for object detection. J Low Power Electron Appl 12(4):51","journal-title":"J Low Power Electron Appl"},{"key":"19145_CR39","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"YF Zhang","year":"2022","unstructured":"Zhang YF, Ren W, Zhang Z, Jia Z, Wang L, Tan T (2022) Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506:146\u2013157","journal-title":"Neurocomputing"},{"key":"19145_CR40","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"19145_CR41","unstructured":"Gevorgyan Z (2022) SIoU loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740"},{"key":"19145_CR42","doi-asserted-by":"crossref","unstructured":"Roecker, Max N and Costa, Yandre MG and Britto, Alceu S and Oliveira, Luiz ES and Bertolini, Diego (2019) Vehicle detection and classification in traffic images using convNets with constrained resources. In: 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 83\u201388. IEEE","DOI":"10.1109\/IWSSIP.2019.8787310"},{"key":"19145_CR43","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","volume":"34","author":"S Razakarivony","year":"2016","unstructured":"Razakarivony S, Jurie F (2016) Vehicle detection in aerial imagery: A small target detection benchmark. J Vis Commun Image Represent 34:187\u2013203","journal-title":"J Vis Commun Image Represent"},{"key":"19145_CR44","doi-asserted-by":"crossref","unstructured":"Liu D, Cui Y, Tan W, Chen Y (2021) Sg-net: Spatial granularity network for one-stage video instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9816\u20139825","DOI":"10.1109\/CVPR46437.2021.00969"},{"key":"19145_CR45","doi-asserted-by":"crossref","unstructured":"Walambe R, Marathe A, Kotecha K, Ghinea G, et al. (2021) Lightweight object detection ensemble framework for autonomous vehicles in challenging weather conditions. Comput Intell Neurosci 2021","DOI":"10.1155\/2021\/5278820"},{"key":"19145_CR46","unstructured":"Marathe A, Walambe R, Kotecha K (2022) In rain or shine: Understanding and overcoming dataset bias for improving robustness against weather corruptions for autonomous vehicles. arXiv preprint arXiv:2204.01062"},{"key":"19145_CR47","doi-asserted-by":"crossref","unstructured":"Chabot F, Chaouch M, Rabarisoa J, Teuliere C, Chateau T (2017) Deep manta: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2040\u20132049","DOI":"10.1109\/CVPR.2017.198"},{"key":"19145_CR48","doi-asserted-by":"crossref","unstructured":"Ren J, Chen X, Liu J, Sun W, Pang J, Yan Q, Tai YW, Xu L (2017) Accurate single stage detector using recurrent rolling convolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5420\u20135428","DOI":"10.1109\/CVPR.2017.87"},{"key":"19145_CR49","doi-asserted-by":"crossref","unstructured":"Xiang Y, Choi W, Lin Y, Savarese S (2017) Subcategory-aware convolutional neural networks for object proposals and detection. In: 2017 IEEE winter conference on applications of computer vision (WACV), pp. 924\u2013933. IEEE","DOI":"10.1109\/WACV.2017.108"},{"key":"19145_CR50","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"19145_CR51","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"19145_CR52","doi-asserted-by":"crossref","unstructured":"Law H, Deng J (2018) Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European conference on computer vision (ECCV), pp. 734\u2013750","DOI":"10.1007\/978-3-030-01264-9_45"},{"issue":"1","key":"19145_CR53","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1109\/TIP.2017.2762591","volume":"27","author":"W Chu","year":"2017","unstructured":"Chu W, Liu Y, Shen C, Cai D, Hua XS (2017) Multi-task vehicle detection with region-of-interest voting. IEEE Trans Image Process 27(1):432\u2013441","journal-title":"IEEE Trans Image Process"},{"key":"19145_CR54","doi-asserted-by":"crossref","unstructured":"Zhang S, Wen L, Bian X, Lei Z, Li SZ (2018) Single-shot refinement neural network for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4203\u20134212","DOI":"10.1109\/CVPR.2018.00442"},{"key":"19145_CR55","doi-asserted-by":"crossref","unstructured":"He Y, Zhu C, Wang J, Savvides M, Zhang X (2019) Bounding box regression with uncertainty for accurate object detection. In: Proceedings of the ieee\/cvf conference on computer vision and pattern recognition, pp. 2888\u20132897","DOI":"10.1109\/CVPR.2019.00300"},{"key":"19145_CR56","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1609\/aaai.v33i01.33018577","volume":"33","author":"B Li","year":"2019","unstructured":"Li B, Liu Y, Wang X (2019) Gradient harmonized single-stage detector. Proceedings of the AAAI conference on artificial intelligence 33:8577\u20138584","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"19145_CR57","doi-asserted-by":"crossref","unstructured":"Zhu X, Hu H, Lin S, Dai J (2019) Deformable convnets v2: More deformable, better results. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9308\u20139316","DOI":"10.1109\/CVPR.2019.00953"},{"key":"19145_CR58","doi-asserted-by":"crossref","unstructured":"Wang X, Cai Z, Gao D, Vasconcelos N (2019) Towards universal object detection by domain attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 7289\u20137298","DOI":"10.1109\/CVPR.2019.00746"},{"key":"19145_CR59","doi-asserted-by":"publisher","first-page":"9259","DOI":"10.1609\/aaai.v33i01.33019259","volume":"33","author":"Q Zhao","year":"2019","unstructured":"Zhao Q, Sheng T, Wang Y, Tang Z, Chen Y, Cai L, Ling H (2019) M2det: A single-shot object detector based on multi-level feature pyramid network. Proceedings of the AAAI conference on artificial intelligence 33:9259\u20139266","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"issue":"3","key":"19145_CR60","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1109\/TITS.2018.2838132","volume":"20","author":"X Hu","year":"2018","unstructured":"Hu X, Xu X, Xiao Y, Chen H, He S, Qin J, Heng PA (2018) SINet: A scale-insensitive convolutional neural network for fast vehicle detection. IEEE Trans Intell Transp Syst 20(3):1010\u20131019","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19145_CR61","doi-asserted-by":"crossref","unstructured":"Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 6054\u20136063","DOI":"10.1109\/ICCV.2019.00615"},{"key":"19145_CR62","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1109\/TIP.2019.2947806","volume":"29","author":"H Zhang","year":"2019","unstructured":"Zhang H, Tian Y, Wang K, Zhang W, Wang FY (2019) Mask SSD: An effective single-stage approach to object instance segmentation. IEEE Trans Image Process 29:2078\u20132093","journal-title":"IEEE Trans Image Process"},{"key":"19145_CR63","doi-asserted-by":"crossref","unstructured":"Choi J, Chun D, Kim H, Lee HJ (2019) Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 502\u2013511","DOI":"10.1109\/ICCV.2019.00059"},{"issue":"9","key":"19145_CR64","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2009","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2009) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627\u20131645","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"19145_CR65","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28"},{"key":"19145_CR66","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"19145_CR67","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104:154\u2013171","journal-title":"Int J Comput Vis"},{"key":"19145_CR68","doi-asserted-by":"crossref","unstructured":"Wang L, Lu Y, Wang H, Zheng Y, Ye H, Xue X (2017) Evolving boxes for fast vehicle detection. In: 2017 IEEE international conference on multimedia and Expo (ICME), pp. 1135\u20131140. IEEE","DOI":"10.1109\/ICME.2017.8019461"},{"key":"19145_CR69","unstructured":"Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. Adv Neural Inf Process Syst 29"},{"key":"19145_CR70","doi-asserted-by":"crossref","unstructured":"Liu W, Liao S, Ren W, Hu W, Yu Y (2019) High-level semantic feature detection: A new perspective for pedestrian detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5187\u20135196","DOI":"10.1109\/CVPR.2019.00533"},{"key":"19145_CR71","doi-asserted-by":"crossref","unstructured":"Amin S, Galasso F (2017) Geometric proposals for faster R-CNN. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20136. IEEE","DOI":"10.1109\/AVSS.2017.8078518"},{"issue":"12","key":"19145_CR72","doi-asserted-by":"publisher","first-page":"6077","DOI":"10.1109\/TIP.2019.2922095","volume":"28","author":"Z Fu","year":"2019","unstructured":"Fu Z, Chen Y, Yong H, Jiang R, Zhang L, Hua XS (2019) Foreground gating and background refining network for surveillance object detection. IEEE Trans Image Process 28(12):6077\u20136090","journal-title":"IEEE Trans Image Process"},{"key":"19145_CR73","doi-asserted-by":"crossref","unstructured":"He CH, Lam KM (2018) Fast vehicle detection with lateral convolutional neural network. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2341\u20132345. IEEE","DOI":"10.1109\/ICASSP.2018.8461874"},{"issue":"13","key":"19145_CR74","doi-asserted-by":"publisher","first-page":"3094","DOI":"10.1049\/ipr2.12297","volume":"15","author":"M Zhao","year":"2021","unstructured":"Zhao M, Zhong Y, Sun D, Chen Y (2021) Accurate and efficient vehicle detection framework based on SSD algorithm. IET Image Process 15(13):3094\u20133104","journal-title":"IET Image Process"},{"issue":"2","key":"19145_CR75","doi-asserted-by":"publisher","first-page":"724","DOI":"10.3390\/s23020724","volume":"23","author":"J Wang","year":"2023","unstructured":"Wang J, Dong Y, Zhao S, Zhang Z (2023) A high-precision vehicle detection and tracking method based on the attention mechanism. Sensors 23(2):724","journal-title":"Sensors"},{"key":"19145_CR76","doi-asserted-by":"crossref","unstructured":"Siddique A, Afanasyev I (2021) Deep learning-based trajectory estimation of vehicles in crowded and crossroad scenarios. In: 2021 28th Conference of Open Innovations Association (FRUCT), pp. 413\u2013423. IEEE","DOI":"10.23919\/FRUCT50888.2021.9347580"},{"key":"19145_CR77","doi-asserted-by":"crossref","unstructured":"Yadav VK, Yadav P, Sharma S (2021) An Efficient Road Surveillance Approach to Detect, Recognize & Tracking Vehicles Using Deep Learning Methods","DOI":"10.32628\/CSEIT2174106"},{"issue":"4","key":"19145_CR78","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.1016\/j.dt.2020.10.006","volume":"17","author":"Jq Luo","year":"2021","unstructured":"Luo Jq, Hs Fang, Fm Shao, Zhong Y, Hua X (2021) Multi-scale traffic vehicle detection based on faster r-cnn with NAS optimization and feature enrichment. Def Technol 17(4):1542\u20131554","journal-title":"Def Technol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19145-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19145-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19145-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T02:47:26Z","timestamp":1739414846000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19145-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,2]]},"references-count":78,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["19145"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19145-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,2]]},"assertion":[{"value":"3 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}