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Mater. Contin."},{"key":"ref4","first-page":"12064","article-title":"Fast and precise: Parallel processing of vehicle traffic videos using big data analytics","volume":"23","author":"P. Juan","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s11063-019-10115-8","article-title":"Traffic signs detection for real-world application of an advanced driving assisting system using deep learning","volume":"51","author":"Ayachi","year":"Feb. 2020","journal-title":"Neural Process. Lett."},{"key":"ref6","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref7","unstructured":"C. Li et al., \u201cYOLOv6: A single-stage object detection framework for industrial applications,\u201d 2022, arXiv:2209.02976."},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1142\/S0218001421500245","article-title":"Real-time implementation of traffic signs detection and identification application on graphics processing units","volume":"35","author":"Ayachi","year":"2021","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref9","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1089\/big.2019.0093","article-title":"A convolutional neural network to perform object detection and identification in visual large-scale data","volume":"9","author":"Ayachi","year":"Mar. 2021","journal-title":"Big Data"},{"key":"ref10","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. 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Pattern Recognit.","first-page":"7241","article-title":"All you need is a few shifts: Designing efficient convolutional neural networks for image classification","author":"Chen","year":"Jun. 16\u201320, 2019"},{"key":"ref16","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"815","article-title":"On the integration of self-attention and convolution","author":"Pan","year":"Jun. 19\u201324, 2022"},{"key":"ref17","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"16519","article-title":"Bottleneck transformers for visual recognition","author":"Srinivas","year":"Jun. 19\u201325, 2021"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1049\/iet-its.2018.5316","article-title":"Vehicle classification approach based on the combined texture and shape features with a compressive DL","volume":"13","author":"Sun","year":"Nov. 2019","journal-title":"IET Intell. Transp. 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Pattern Recognit.","first-page":"8759","article-title":"Path aggregation network for instance segmentation","author":"Liu","year":"Jun. 18\u201323, 2018"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"145740","DOI":"10.1109\/ACCESS.2020.3014910","article-title":"Small-object detection in UAV-captured images via multi-branch parallel feature pyramid networks","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref24","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis. Workshops","article-title":"Dense and small object detection in UAV vision based on cascade network","author":"Zhang","year":"Oct. 27\u2013Nov. 2, 2019"},{"key":"ref25","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"7036","article-title":"NAS-FPN: Learning scalable feature pyramid architecture for object detection","author":"Ghiasi","year":"Jun. 16\u201320, 2019"},{"key":"ref26","series-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","first-page":"6649","article-title":"Auto-FPN: Automatic network architecture adaptation for object detection beyond classification","author":"Xu","year":"Oct. 27\u2013Nov. 2, 2019"},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.3390\/rs14030620","article-title":"Urban traffic monitoring and analysis using unmanned aerial vehicles (UAVs): A systematic literature review","volume":"14","author":"Butil\u0103","year":"2022","journal-title":"Remote Sens."},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.3390\/app12167966","article-title":"Traffic monitoring from the perspective of an unmanned aerial vehicle","volume":"12","author":"Kainz","year":"2022","journal-title":"Appl. Sci."},{"key":"ref29","unstructured":"A. Bochkovskiy, C. -Y. Wang, and H. -Y. M. Liao, \u201cYOLOv4: Optimal speed and accuracy of object detection,\u201d 2020, arXiv:2004.10934."},{"key":"ref30","first-page":"1","article-title":"Vehicle image detection method using deep learning in UAV video","volume":"2022","author":"Wang","year":"Jan. 2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref31","series-title":"Comput. Vis.-ECCV 2016: 14th European Conf.","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"Liu","year":"Oct. 11\u201314, 2016"},{"key":"ref32","series-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis.","first-page":"2119","article-title":"AirCamRTM: Enhancing vehicle detection for efficient aerial camera-based road traffic monitoring","author":"Makrigiorgis","year":"Jan. 4\u20138, 2022"},{"key":"ref33","series-title":"Computer Vision and Pattern Recognition","first-page":"1","article-title":"YOLOv3: An incremental improvement","volume":"1804","author":"Farhadi","year":"2018"},{"key":"ref34","first-page":"263","article-title":"ERFNet: Efficient residual factorized convnet for real-time semantic segmentation","volume":"19","author":"Eduardo","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref35","unstructured":"H. Li, P. Xiong, J. An, and L. Wang, \u201cPyramid attention network for semantic segmentation,\u201d  arXiv:1805.10180, 2018."},{"key":"ref36","unstructured":"L. -C. Chen, G. Papandreou, F. Schroff, and H. Adam, \u201cRethinking atrous convolution for semantic image segmentation,\u201d 2017, arXiv:1706.05587."},{"key":"ref37","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112015","article-title":"Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration","volume":"164","author":"Bakirci","year":"Jan. 2024","journal-title":"Appl. Soft Comput."},{"key":"ref38","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11063-024-11643-8","article-title":"Add-Vit: CNN-Transformer hybrid architecture for small data paradigm processing","volume":"56","author":"Chen","year":"Apr. 2024","journal-title":"Neural Process. Lett."},{"key":"ref39","unstructured":"O. Havryliuk, \u201cAutomated annotation scheme for extending bounding box representation to detect ship locations,\u201d  M.S. thesis, Sch. of Innov. Design Eng., M\u00e4lardalen Univ., Vasteras, Sweden, 2023."},{"key":"ref40","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"ref41","series-title":"Proc. 2024 Int. Conf. Adv. Data Eng. Intell. Comput. Syst. 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