Proceedings ArticleDOI
Dot Distance for Tiny Object Detection in Aerial Images
Chang Xu,Jinwang Wang,Wen Yang,Lei Yu +3 more
- pp 1192-1201
TLDR
DotD as discussed by the authors is defined as normalized Euclidean distance between the center points of two bounding boxes, which is a new metric for tiny object detection where anchor-based and anchor-free detectors are used.Abstract:
Object detection has achieved great progress with the development of anchor-based and anchor-free detectors. However, the detection of tiny objects is still challenging due to the lack of appearance information. In this paper, we observe that Intersection over Union (IoU), the most widely used metric in object detection, is sensitive to slight offsets between predicted bounding boxes and ground truths when detecting tiny objects. Although some new metrics such as GIoU, DIoU and CIoU are proposed, their performance on tiny object detection is still below the expected level by a large margin. In this paper, we propose a simple but effective new metric called Dot Distance (DotD) for tiny object detection where DotD is defined as normalized Euclidean distance between the center points of two bounding boxes. Extensive experiments on tiny object detection dataset show that anchor-based detectors’ performance is highly improved over their baselines with the application of DotD.read more
Citations
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Journal ArticleDOI
Towards Large-Scale Small Object Detection: Survey and Benchmarks
TL;DR: Two large-scale Small Object Detection dAtasets (SODA), SODA-D and S ODA-A, which focus on the Driving and Aerial scenarios respectively are constructed, and the performance of mainstream methods on SOD a is evaluated.
Proceedings ArticleDOI
RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection
TL;DR: A Gaussian Receptive Field based Label Assignment (RFLA) strategy for tiny object detection that outperforms the state-of-the-art competitors with 4.0 AP points on the AI-TOD dataset and designs a Hierarchical Label assignment module based on RFD to achieve balanced learning for tiny objects.
Journal ArticleDOI
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
TL;DR: Zhang et al. as discussed by the authors proposed Normalized Wasserstein Distance (NWD) and RanKing-based Assignment (RKA) strategy for tiny object detection.
Journal ArticleDOI
Automatic Detection and Segmentation of Thrombi in Abdominal Aortic Aneurysms Using a Mask Region-Based Convolutional Neural Network with Optimized Loss Functions
TL;DR: This work proposes a fully automated method for the whole process of the detection and segmentation of thrombi, which is based on a well-established mask region-based convolutional neural network (Mask R-CNN) framework that is improved with optimized loss functions.
Journal ArticleDOI
RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection
TL;DR: Zhang et al. as discussed by the authors proposed a Gaussian Receptive Field based Label Assignment (RFLA) strategy for tiny object detection, which utilizes the prior information that the feature receptive field follows Gaussian distribution.
References
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Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
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Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
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Journal ArticleDOI
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