{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/car-detection-using-unmanned-aerial-vehicles","title":"Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3","arxiv_id":"1812.10968","date":"2018-12-28","proceeding":null,"authors":["Bilel Benjdira","Taha Khursheed","Anis Koubaa","Adel Ammar","Kais Ouni"],"abstract":"Unmanned Aerial Vehicles are increasingly being used in surveillance and\ntraffic monitoring thanks to their high mobility and ability to cover areas at\ndifferent altitudes and locations. One of the major challenges is to use aerial\nimages to accurately detect cars and count them in real-time for traffic\nmonitoring purposes. Several deep learning techniques were recently proposed\nbased on convolution neural network (CNN) for real-time classification and\nrecognition in computer vision. However, their performance depends on the\nscenarios where they are used. In this paper, we investigate the performance of\ntwo state-of-the-art CNN algorithms, namely Faster R-CNN and YOLOv3, in the\ncontext of car detection from aerial images. We trained and tested these two\nmodels on a large car dataset taken from UAVs. We demonstrated in this paper\nthat YOLOv3 outperforms Faster R-CNN in sensitivity and processing time,\nalthough they are comparable in the precision metric.","url_abs":"http://arxiv.org/abs/1812.10968v1","url_pdf":"http://arxiv.org/pdf/1812.10968v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"car-detection-using-unmanned-aerial-vehicles","repo_url":"https://github.com/aniskoubaa/car_detection_yolo_faster_rcnn_uvsc2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"logistic-regression","method_name":"Logistic Regression"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"yolov3","method_name":"YOLOv3"},{"method_slug":"k-means-clustering","method_name":"k-Means Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}