{"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/an-accurate-car-counting-in-aerial-images","title":"An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks","arxiv_id":null,"date":"2021-07-13","proceeding":"Journal of Ambient Intelligence and Humanized Computing 2021 7","authors":["Ersin Kılıç","Serkan Öztürk"],"abstract":"This paper proposes a simple and effective single-shot detector model to detect and\r\ncount cars in aerial images. The proposed model, called heatmap learner convolutional\r\nneural network (HLCNN), is used to predict the heatmap of target car instances. In\r\norder to learn the heatmap of the target cars, we have improved CNN architecture by\r\nadding three convolutional layers as adaptation layers instead of fully connected\r\nlayers. The VGG-16 has been used as a backbone convolutional neural network in the\r\nproposed model. The proposed method successfully determines the number of cars\r\nand precisely detects the center of target cars. Experiments on the two different car\r\ndatasets (PUCPR+ and CARPK) show the state-of-the-art counting and localizing\r\nperformance of the proposed method in comparison with existing methods. Also,\r\nexperiments have been conducted to examine the effect of data augmentation and\r\nbatch normalization on the success of the proposed method. The code and data will be\r\nmade available here [https://www.github.com/ekilic/Heatmap-Learner-CNN-for-Object-Counting].","url_abs":"https://link.springer.com/article/10.1007%2Fs12652-021-03377-5","url_pdf":"https://link.springer.com/article/10.1007%2Fs12652-021-03377-5","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":"an-accurate-car-counting-in-aerial-images","repo_url":"https://github.com/ekilic/Heatmap-Learner-CNN-for-Object-Counting","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"object-counting","task_name":"Object Counting"}],"methods":[{"method_slug":"heatmap","method_name":"Heatmap"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-counting-on-carpk","task":"Object Counting","dataset":"CARPK","model":"HLCNN","rank_in_archive_order":1,"of":15,"metrics":{"MAE":"2.12","RMSE":"3.02"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}