{"title":"Hierarchical Active Learning for Low-Altitude Drone-View Object Detection","authors":"Haohao Hu, Tianyu Han, Yuerong Wang, Wanjun Zhong, Jingwei Yue, Peng Zan","doi":"10.1007/s11263-024-02228-y","DOIUrl":null,"url":null,"abstract":"<p>Various object detection techniques are employed on drone platforms. However, the task of annotating drone-view samples is both time-consuming and laborious. This is primarily due to the presence of numerous small-sized instances to be labeled in the drone-view image. To tackle this issue, we propose HALD, a hierarchical active learning approach for low-altitude drone-view object detection. HALD extracts unlabeled image information sequentially from different levels, including point, box, image, and class, aiming to obtain a reliable indicator of image information. The point-level module is utilized to ascertain the valid count and location of instances, while the box-level module screens out reliable predictions. The image-level module selects candidate samples by calculating the consistency of valid boxes within an image, and the class-level module selects the final selected samples based on the distribution of candidate and labeled samples across different classes. Extensive experiments conducted on the VisDrone and CityPersons datasets demonstrate that HALD outperforms several other baseline methods. Additionally, we provide an in-depth analysis of each proposed module. The results show that the performance of evaluating the informativeness of samples can be effectively improved by the four hierarchical levels.\n</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":null,"pages":null},"PeriodicalIF":11.6000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Vision","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11263-024-02228-y","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Various object detection techniques are employed on drone platforms. However, the task of annotating drone-view samples is both time-consuming and laborious. This is primarily due to the presence of numerous small-sized instances to be labeled in the drone-view image. To tackle this issue, we propose HALD, a hierarchical active learning approach for low-altitude drone-view object detection. HALD extracts unlabeled image information sequentially from different levels, including point, box, image, and class, aiming to obtain a reliable indicator of image information. The point-level module is utilized to ascertain the valid count and location of instances, while the box-level module screens out reliable predictions. The image-level module selects candidate samples by calculating the consistency of valid boxes within an image, and the class-level module selects the final selected samples based on the distribution of candidate and labeled samples across different classes. Extensive experiments conducted on the VisDrone and CityPersons datasets demonstrate that HALD outperforms several other baseline methods. Additionally, we provide an in-depth analysis of each proposed module. The results show that the performance of evaluating the informativeness of samples can be effectively improved by the four hierarchical levels.
期刊介绍:
The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs.
Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision.
Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community.
Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas.
In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives.
The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research.
Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.