Bag of Tricks: Semi-Supervised Cross-domain Crater Detection with Poor
  Data Quality
Yifan Liu Tiecheng Song
With the development of spaceflight and the exploration of extraterrestrial
planets, exoplanet crater detection has gradually gained attention. However,
with the current scarcity of relevant datasets, high sample background
complexity, and large inter-domain differences, few existing detection models
can achieve good robustness and generalization across domains by training on
data with more background interference. To obtain a better robust model with
better cross-domain generalization in the presence of poor data quality, we
propose the SCPQ model, in which we first propose a method for fusing shallow
information using attention mechanism (FSIAM), which utilizes feature maps
fused with deep convolved feature maps after fully extracting the global
sensory field of shallow information via the attention mechanism module, which
can fully fit the data to obtain a better sense of the domain in the presence
of poor data, and thus better multiscale adaptability. Secondly, we propose a
pseudo-label and data augment strategy (PDAS) and a smooth hard example mining
(SHEM) loss function to improve cross-domain performance. PDAS adopts
high-quality pseudo-labeled data from the target domain to the finetune model,
and adopts different strong and weak data enhancement strategies for different
domains, which mitigates the different distribution of information inherent in
the source and target domains, and obtains a better generalization effect.
Meanwhile, our proposed SHEM loss function can solve the problem of poor
robustness of hard examples due to partial background interference learning
during the training process. The SHEM loss function can smooth this
interference and has generalization while learning hard examples. Experimental
results show that we achieved better performance on the DACD dataset and
improved the Recall of cross-domain detection by 24.04\% over baseline.