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Keyword: HRSC2016 ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Oriented Object Detection in Aerial Images Based on the Scaled Smooth L1 Loss Function

   Wei, Chen and Hu · Remote Sensing · 2023

   The authors propose a scaled smooth L1 loss and build a two-stage rotated-box detector for aerial images, reporting higher mAP on the DOTA and HRSC2016 datasets than other two-stage oriented methods.

   Unchecked3 claims
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   1. UncheckedThe paper's scaled smooth L1 loss, used to fit bounding boxes around objects, is described as making box regression invariant to object scale.“The proposed scaled smooth L1 loss function is used for bounding box regression and makes regression invariant to scale.”
   2. UncheckedOn the DOTA aerial-image dataset, the authors' detector reaches 70.82 mAP, 1.26 to 16.49 above other two-stage oriented methods they compared with.“Compared to other promising two-stage oriented methods, our method achieves a mAP of 70.82 on DOTA, with an improvement of at least 1.26 and up to 16.49.”
   3. UncheckedThe authors' aerial-image object detector is effective and robust, with the best scale factor for their scaled smooth L1 loss being around 2.0.“The proposed object detector is effective and robust, with the optimal scale factor for the scaled smooth L1 loss function being around 2.0.”