Object detection performance, as measured on the PASCAL VOC dataset, has achieved a prominent result since systems based on the deep convolution neural network (CNN) was proposed. However, inaccurate localization remains a major factor causing error for detection. Building upon high-capacity CNN architectures, we address the problem by 1)combining a high-recall algorithm proposing candidate regions for an object bounding box with an algorithm reducing localization bias, and 2)utilizing box alignment which penalizing deviation via taking object boundaries into account, to instruct the procedure of proposing input of CNN. Experiments demonstrate that the proposed methods improve the detection performance over the baseline and many other methods on the PASCAL VOC 2007 dataset.