  Deep learning techniques have enabled the emergence of state-of-the-art
models to address object detection tasks. However, these techniques are
data-driven, delegating the accuracy to the training dataset which must
resemble the images in the target task. The acquisition of a dataset involves
annotating images, an arduous and expensive process, generally requiring time
and manual effort. Thus, a challenging scenario arises when the target domain
of application has no annotated dataset available, making tasks in such
situation to lean on a training dataset of a different domain. Sharing this
issue, object detection is a vital task for autonomous vehicles where the large
amount of driving scenarios yields several domains of application requiring
annotated data for the training process. In this work, a method for training a
car detection system with annotated data from a source domain (day images)
without requiring the image annotations of the target domain (night images) is
presented. For that, a model based on Generative Adversarial Networks (GANs) is
explored to enable the generation of an artificial dataset with its respective
annotations. The artificial dataset (fake dataset) is created translating
images from day-time domain to night-time domain. The fake dataset, which
comprises annotated images of only the target domain (night images), is then
used to train the car detector model. Experimental results showed that the
proposed method achieved significant and consistent improvements, including the
increasing by more than 10% of the detection performance when compared to the
training with only the available annotated data (i.e., day images).
