Focus And Detect A Small Object Detection Framework For Aerial Images Pdf To address this gap, we develop the xs vid dataset, which comprises aerial data from various periods and scenes, and annotates eight major object categories. Xs vid is a comprehensive dataset for extra small object video detection, including diverse day and night scenes such as rivers, forests, skyscrapers, and streets.
Uav Small Object Detection Dataset Kaggle Update [20250303] we have provided a new download of xs vidv2 with the v1 data merged to form a new dataset, with a convenient command line download: xs vid! [20250301] we have released xs vidv2 containing 374 videos with 186446 frames for training 36478 frames for testing!. To address these issues, we propose a video object detection dataset, xs vid. xs vid includes 12k frames and 38 medium to long sequence videos, covering multiple object sizes across 10 types of scenes, including rivers, forests, skyscrapers, and roads, at various times of day and night. To address this gap, we develop the xs vid dataset, which comprises aerial data from various periods and scenes, and annotates eight major object categories. This research introduces xs vid, a comprehensive dataset specifically designed for small video object detection (svod), along with yoloft, a detection method that integrates temporal motion features to improve performance on tiny objects.
Pdf Xs Vid An Extremely Small Video Object Detection Dataset To address this gap, we develop the xs vid dataset, which comprises aerial data from various periods and scenes, and annotates eight major object categories. This research introduces xs vid, a comprehensive dataset specifically designed for small video object detection (svod), along with yoloft, a detection method that integrates temporal motion features to improve performance on tiny objects. This work proposes a hybrid detector, called rrnet, for object detection in city scenes usually come in various sizes and are extremely dense, and demonstrates that rrnet significantly outperforms all the state of the art detectors on visdrone2018 dataset.
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