Most prevailing object detection methods share networks and features between localization and classification components, which easily leads to sub-optimal learning for the two separate tasks. In this paper, we propose a conception of task differentiation and design specialized sub-networks for both localization and classification components based on SSD framework. A novel probability based localization method is introduced into the one-stage framework and combined with bounding box regression for precise object localization. Furthermore, a new feature fusion strategy, together with a global attention mechanism, is proposed to learn more robust features. Experimental results on PASCAL VOC and MS COCO data sets indicate that our method has impressive performance compared with other state-of-the-art object detection approaches.

大多数流行的对象检测方法在定位和分类组件之间共享网络和功能，这很容易导致针对两个单独任务的次优学习。在本文中，我们提出了任务区分的概念，并基于SSD框架为定位和分类组件设计了专门的子网。一种新颖的基于概率的定位方法被引入到一阶段框架中，并与包围盒回归相结合以实现精确的对象定位。此外，提出了一种新的特征融合策略，以及一种全球关注机制，以学习更强大的特征。在PASCAL VOC和MS COCO数据集上的实验结果表明，与其他最新的对象检测方法相比，我们的方法具有令人印象深刻的性能。