MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing
for Active Annotation in Aerial Object Detection
- URL: http://arxiv.org/abs/2212.02804v4
- Date: Tue, 19 Sep 2023 09:46:28 GMT
- Title: MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing
for Active Annotation in Aerial Object Detection
- Authors: Dong Liang and Jing-Wei Zhang and Ying-Peng Tang and Sheng-Jun Huang
- Abstract summary: Recent aerial object detection models rely on a large amount of labeled training data.
Active learning effectively reduces the data labeling cost by selectively querying the informative and representative unlabelled samples.
We propose a novel active learning method for cost-effective aerial object detection.
- Score: 40.94800050576902
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent aerial object detection models rely on a large amount of labeled
training data, which requires unaffordable manual labeling costs in large
aerial scenes with dense objects. Active learning effectively reduces the data
labeling cost by selectively querying the informative and representative
unlabelled samples. However, existing active learning methods are mainly with
class-balanced settings and image-based querying for generic object detection
tasks, which are less applicable to aerial object detection scenarios due to
the long-tailed class distribution and dense small objects in aerial scenes. In
this paper, we propose a novel active learning method for cost-effective aerial
object detection. Specifically, both object-level and image-level
informativeness are considered in the object selection to refrain from
redundant and myopic querying. Besides, an easy-to-use class-balancing
criterion is incorporated to favor the minority objects to alleviate the
long-tailed class distribution problem in model training. We further devise a
training loss to mine the latent knowledge in the unlabeled image regions.
Extensive experiments are conducted on the DOTA-v1.0 and DOTA-v2.0 benchmarks
to validate the effectiveness of the proposed method. For the ReDet, KLD, and
SASM detectors on the DOTA-v2.0 dataset, the results show that our proposed
MUS-CDB method can save nearly 75\% of the labeling cost while achieving
comparable performance to other active learning methods in terms of mAP.Code is
publicly online (https://github.com/ZJW700/MUS-CDB).
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