IoU-balanced Loss Functions for Single-stage Object Detection
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Single-stage detectors are efficient. However, we find that the loss functions adopted by single-stage detectors are sub-optimal for accurate localization. The standard cross entropy loss for classification is independent of localization task and drives all the positive examples to learn as high classification score as possible regardless of localization accuracy during training. As a result, there will be detections that have high classification score but low IoU or low classification score but high IoU. And the detections with low classification score but high IOU will be suppressed by the ones with high classification score but low IOU during NMS, hurting the localization accuracy. For the standard smooth L1 loss, the gradient is dominated by the outliers that have poorly localization accuracy and this is harmful for accurate localization. In this work, we propose IoU-balanced loss functions that consist of IoU-balanced classification loss and IoU-balanced localization loss to solve the above problems. The IoU-balanced classification loss focuses more attention on positive examples with high IOU and can enhance the correlation between classification and localization task. The IoU-balanced localization loss decreases the gradient of the examples with low IoU and increases the gradient of examples with high IoU, which can improve the localization accuracy of models. Sufficient studies on MS COCO demonstrate that both IoU-balanced classification loss and IoU-balanced localization loss can bring substantial improvement for the single-stage detectors. Without whistles and bells, the proposed methods can improve AP by 1.1 detectors and the improvement for AP at higher IoU threshold is especially large, such as 2.3
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