S-OHEM: Stratified Online Hard Example Mining for Object Detection

One of the major challenges in object detection is to propose detectors with highly accurate localization of objects. The online sampling of high-loss region proposals (hard examples) has made training of region-based deep convolutional network detectors effective and efficient. In this paper, we present the Stratified Online Hard Example Mining (S-OHEM) algorithm for training higher efficiency and accuracy detectors. S-OHEM exploits OHEM with stratified sampling, a widely adopted sampling technique. OHEM uses the multitask loss with equal weight settings across all loss types (e.g., classification and localization, rigid and non-rigid categories) and ignores the influence of different loss distribution throughout the training process, which we found essential to the training efficacy. By maintaining a sampling distribution according to this influence during hard example mining, we can enhance the performance of object detectors. Based on this, S-OHEM samples the training examples according to this distribution before feeding them to the backpropagation process. We show through systematic experiments that S-OHEM yields an average precision (AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric are 1.6%. Regarding the mean average precision (mAP), a relative increase of 0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based detectors and is capable of acting with post-recognition level regressors.