Class-discriminative focal loss for extreme imbalanced multiclass object detection towards autonomous driving

Abstract

Currently, modern object detection algorithms still suffer the imbalance problems especially the foreground–background and foreground–foreground class imbalance. Existing methods generally adopt re-sampling based on the class frequency or re-weighting based on the category prediction probability, such as focal loss, proposed to rebalance the loss assigned to easy negative examples and hard positive examples for single-stage detectors. However, there are still two critical issues unresolved. In practical applications, such as autonomous driving, the class imbalance will become more extreme due to the increased detection field and target distribution characteristics, needing a more effective way to balance the foreground–background class imbalance. Besides, existing methods typically employ the sigmoid or softmax entropy loss for classification task, which we believe is not capable to realize the foreground–foreground class balance. In this paper, we propose a new form of focal loss by re-designing the re-weighting scheme that can calculate the weight according to the probability as well as widen the weight difference of the examples. Besides, we introduce the extended focal loss to multi-class classification task by reformulating the standard softmax cross-entropy loss for better utilizing the discriminant difference of foreground categories, thereby yielding a class-discriminative focal loss. Comprehensive experiments are conducted on the KITTI and BDD dataset, respectively. The results show that our approach can easily surpass focal loss with no more training and inference time cost. Besides, when trained with the proposed loss function, current state-of-the-art object detectors no matter in one-stage or two-stage paradigms can achieve significant performance gains.