Cascaded convolutional neural networks for object detection
Yajing Guo, Xiaoqiang Guo, Zhuqing Jiang, Yun Zhou · 2017
Recent advances in object detection depend on region proposal algorithms or networks to predict object locations. The pipeline of region proposal-based object detection can be decomposed into two cascaded sub-tasks: 1) region proposals generation from input image, 2) proposals classification into various object categories. In this paper, we propose cascaded convolutional neural networks to make improvement for two sub-tasks respectively. For the region proposals generation stage, we add a RefineNet after the original region proposal network(RPN) to make the proposals more compact and better located. For the classification stage, we integrate a binary classifier for each object class into the network which makes the feature representation capture more intra-class variance. Experiments on PASCAL VOC dataset demonstrate that our approach can achieve considerable improvement over state-of-the-art object detectors.