  Acquiring fine-grained object detection annotations in unconstrained images
is time-consuming, expensive, and prone to noise, especially in crowdsourcing
scenarios. Most prior object detection methods assume accurate annotations; A
few recent works have studied object detection with noisy crowdsourced
annotations, with evaluation on distinct synthetic crowdsourced datasets of
varying setups under artificial assumptions. To address these algorithmic
limitations and evaluation inconsistency, we first propose a novel Bayesian
Detector Combination (BDC) framework to more effectively train object detectors
with noisy crowdsourced annotations, with the unique ability of automatically
inferring the annotators' label qualities. Unlike previous approaches, BDC is
model-agnostic, requires no prior knowledge of the annotators' skill level, and
seamlessly integrates with existing object detection models. Due to the
scarcity of real-world crowdsourced datasets, we introduce large synthetic
datasets by simulating varying crowdsourcing scenarios. This allows consistent
evaluation of different models at scale. Extensive experiments on both real and
synthetic crowdsourced datasets show that BDC outperforms existing
state-of-the-art methods, demonstrating its superiority in leveraging
crowdsourced data for object detection. Our code and data are available at
https://github.com/zhiqin1998/bdc.
