  Object detection has achieved remarkable progress in the past decade.
However, the detection of oriented and densely packed objects remains
challenging because of following inherent reasons: (1) receptive fields of
neurons are all axis-aligned and of the same shape, whereas objects are usually
of diverse shapes and align along various directions; (2) detection models are
typically trained with generic knowledge and may not generalize well to handle
specific objects at test time; (3) the limited dataset hinders the development
on this task. To resolve the first two issues, we present a dynamic refinement
network that consists of two novel components, i.e., a feature selection module
(FSM) and a dynamic refinement head (DRH). Our FSM enables neurons to adjust
receptive fields in accordance with the shapes and orientations of target
objects, whereas the DRH empowers our model to refine the prediction
dynamically in an object-aware manner. To address the limited availability of
related benchmarks, we collect an extensive and fully annotated dataset,
namely, SKU110K-R, which is relabeled with oriented bounding boxes based on
SKU110K. We perform quantitative evaluations on several publicly available
benchmarks including DOTA, HRSC2016, SKU110K, and our own SKU110K-R dataset.
Experimental results show that our method achieves consistent and substantial
gains compared with baseline approaches. The code and dataset are available at
https://github.com/Anymake/DRN_CVPR2020.
