  Deep learning has become the dominating approach for object detection. To
achieve accurate fine-grained detection, one needs to employ a large enough
model and a vast amount of data annotations. In this paper, we propose a
commonsense knowledge inference module (CKIM) which leverages commonsense
knowledge to assist a lightweight deep neural network base coarse-grained
object detector to achieve accurate fine-grained detection. Specifically, we
focus on a scenario where a single image contains objects of similar categories
but varying sizes, and we establish a size-related commonsense knowledge
inference module (CKIM) that maps the coarse-grained labels produced by the DL
detector to size-related fine-grained labels. Considering that rule-based
systems are one of the popular methods of knowledge representation and
reasoning, our experiments explored two types of rule-based CKIMs, implemented
using crisp-rule and fuzzy-rule approaches, respectively. Experimental results
demonstrate that compared with baseline methods, our approach achieves accurate
fine-grained detection with a reduced amount of annotated data and smaller
model size. Our code is available at: https://github.com/ZJLAB-AMMI/CKIM.
