中国科学院自动化研究所机构知识库

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 which 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. Our
source code and dataset will be released to encourage followup
research.