Nearest-neighbor interpolation matches multi-scale deep super-resolution performance for vehicle detection on 4x-upscaled xView satellite imagery, with a 0.0002 AP difference.
Reduced Focal Loss: 1st Place Solution to xView object detection in Satellite Imagery
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abstract
This paper describes our approach to the DIUx xView 2018 Detection Challenge [1]. This challenge focuses on a new satellite imagery dataset. The dataset contains 60 object classes that are highly imbalanced. Due to the imbalanced nature of the dataset, the training process becomes significantly more challenging. To address this problem, we introduce a novel Reduced Focal Loss function, which brought us 1st place in the DIUx xView 2018 Detection Challenge.
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A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data
Nearest-neighbor interpolation matches multi-scale deep super-resolution performance for vehicle detection on 4x-upscaled xView satellite imagery, with a 0.0002 AP difference.