论文标题
一般对象检测网络的零成本提高
Zero Cost Improvements for General Object Detection Network
论文作者
论文摘要
现代对象检测网络追求一般对象检测数据集上的更高精度，同时，随着精度的提高，计算负担也在增加。然而，推理时间和精度对于需要实时的对象检测系统至关重要。在没有额外的计算成本的情况下，必须研究精确改进。在这项工作中，提出了两个模块，以提高检测精度，零成本，这些模块的重点是FPN和通用对象检测网络的检测头改进。我们采用比例注意机制将多级特征图与更少的参数融合在一起，这称为SA-FPN模块。考虑到分类头和回归头的相关性，我们使用顺序的头代替了广泛使用的并行头，这称为Seq-Head模块。为了评估有效性，我们将两个模块应用于某些现代的对象检测网络，包括基于锚的和无锚。可可数据集的实验结果表明，具有两个模块的网络可以超过1.1 AP和0.8 AP的原始网络，而基于锚的无锚网和无锚网的成本为零。代码将在https://git.io/jtfgl上找到。
Modern object detection networks pursuit higher precision on general object detection datasets, at the same time the computation burden is also increasing along with the improvement of precision. Nevertheless, the inference time and precision are both critical to object detection system which needs to be real-time. It is necessary to research precision improvement without extra computation cost. In this work, two modules are proposed to improve detection precision with zero cost, which are focus on FPN and detection head improvement for general object detection networks. We employ the scale attention mechanism to efficiently fuse multi-level feature maps with less parameters, which is called SA-FPN module. Considering the correlation of classification head and regression head, we use sequential head to take the place of widely-used parallel head, which is called Seq-HEAD module. To evaluate the effectiveness, we apply the two modules to some modern state-of-art object detection networks, including anchor-based and anchor-free. Experiment results on coco dataset show that the networks with the two modules can surpass original networks by 1.1 AP and 0.8 AP with zero cost for anchor-based and anchor-free networks, respectively. Code will be available at https://git.io/JTFGl.
