Constellation Dataset: Benchmarking High-Altitude Object Detection for an Urban Intersection
arxiv(2024)
摘要
We introduce Constellation, a dataset of 13K images suitable for research on
detection of objects in dense urban streetscapes observed from high-elevation
cameras, collected for a variety of temporal conditions. The dataset addresses
the need for curated data to explore problems in small object detection
exemplified by the limited pixel footprint of pedestrians observed tens of
meters from above. It enables the testing of object detection models for
variations in lighting, building shadows, weather, and scene dynamics. We
evaluate contemporary object detection architectures on the dataset, observing
that state-of-the-art methods have lower performance in detecting small
pedestrians compared to vehicles, corresponding to a 10
precision (AP). Using structurally similar datasets for pretraining the models
results in an increase of 1.8
incorporating domain-specific data augmentations helps improve model
performance. Using pseudo-labeled data, obtained from inference outcomes of the
best-performing models, improves the performance of the models. Finally,
comparing the models trained using the data collected in two different time
intervals, we find a performance drift in models due to the changes in
intersection conditions over time. The best-performing model achieves a
pedestrian AP of 92.0
mAP of 95.4
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