  Object detection is an important task in computer vision which serves a lot
of real-world applications such as autonomous driving, surveillance and
robotics. Along with the rapid thrive of large-scale data, numerous
state-of-the-art generalized object detectors (e.g. Faster R-CNN, YOLO, SSD)
were developed in the past decade. Despite continual efforts in model
modification and improvement in training strategies to boost detection
accuracy, there are still limitations in performance of detectors when it comes
to specialized datasets with uneven object class distributions. This originates
from the common usage of Cross Entropy loss function for object classification
sub-task that simply ignores the frequency of appearance of object class during
training, and thus results in lower accuracies for object classes with fewer
number of samples. Class-imbalance in general machine learning has been widely
studied, however, little attention has been paid on the subject of object
detection. In this paper, we propose to explore and overcome such problem by
application of several weighted variants of Cross Entropy loss, for examples
Balanced Cross Entropy, Focal Loss and Class-Balanced Loss Based on Effective
Number of Samples to our object detector. Experiments with BDD100K (a highly
class-imbalanced driving database acquired from on-vehicle cameras capturing
mostly Car-class objects and other minority object classes such as Bus, Person
and Motor) have proven better class-wise performances of detector trained with
the afore-mentioned loss functions.
