Offset Bin Classification Network for Accurate Object Detection

Abstract

Object detection combines object classification and object localization problems. Most existing object detection methods usually locate objects by leveraging regression networks trained with Smooth L1 loss function to predict offsets between candidate boxes and objects. However, this loss function applies the same penalties on different samples with large errors, which results in suboptimal regression networks and inaccurate offsets. In this paper, we propose an offset bin classification network optimized with cross entropy loss to predict more accurate offsets. It not only provides different penalties for different samples but also avoids the gradient explosion problem caused by the samples with large errors. Specifically, we discretize the continuous offset into a number of bins, and predict the probability of each offset bin. Furthermore, we propose an expectation-based offset prediction and a hierarchical focusing method to improve the prediction precision. Extensive experiments on the PASCAL VOC and MS-COCO datasets demonstrate the effectiveness of our proposed method. Our method outperforms the baseline methods by a large margin.