  Candidate object proposals generated by object detectors based on
convolutional neural network (CNN) encounter easy-hard samples imbalance
problem, which can affect overall performance. In this study, we propose a
Proposal-balanced Network (ProbaNet) for alleviating the imbalance problem.
Firstly, ProbaNet increases the probability of choosing hard samples for
training by discarding easy samples through threshold truncation. Secondly,
ProbaNet emphasizes foreground proposals by increasing their weights. To
evaluate the effectiveness of ProbaNet, we train models based on different
benchmarks. Mean Average Precision (mAP) of the model using ProbaNet achieves
1.2$\%$ higher than the baseline on PASCAL VOC 2007. Furthermore, it is
compatible with existing two-stage detectors and offers a very small amount of
additional computational cost.
