Convolutional neural networks-based object detection techniques have achieved positive performances. However, due to the limitations of local receptive field, some existing object detection methods cannot effectively capture global information in feature extraction phases, and thus lead to unsatisfactory detection performance. Moreover, the feature information extracted by the backbone network may be redundant. To alleviate these problems, in this paper we propose a refined feature enhancement network (RFENet) for object detection. Specifically, we first propose a feature enhancement module (FEM) to capture more global and local information from feature maps with certain long-range dependencies. We further propose a multi-branch dilated attention mechanism (MDAM) to refine the extracted features in a weighted form, which can select more important spatial and channel information and broaden the receptive field of the network. Finally, we validate RFENet on MS-COCO2017, PASCAL VOC2012, and PASCAL VOC07+12 datasets, respectively. Compared to the baseline network, our RFENet improves by 2.4 AP on MS-COCO2017 dataset, 3.4 mAP on PASCAL VOC2012 dataset, and 2.7 mAP on PASCAL VOC07+12 dataset. Extensive experiments show that our RFENet can perform competitively on different datasets. The code is available at https://github.com/object9detection/RFENet.

基于卷积神经网络的对象检测技术取得了积极的性能。然而，由于局部感受野的限制，一些现有的目标检测方法在特征提取阶段无法有效捕获全局信息，从而导致检测性能不尽如人意。此外，骨干网络提取的特征信息可能是冗余的。为了缓解这些问题，在本文中，我们提出了一种用于对象检测的精细特征增强网络 （RFENet）。具体来说，我们首先提出了一个特征增强模块（FEM），从具有某些长距离依赖关系的特征图中捕获更多的全局和局部信息。我们进一步提出了一种多分支扩张注意力机制 （MDAM），以加权形式提炼提取的特征，可以选择更重要的空间和通道信息，拓宽网络的感受野。最后，我们分别在 MS-COCO2017 、 PASCAL VOC2012 和 PASCAL VOC07+12 数据集上验证了 RFENet。与基线网络相比，我们的 RFENet 在 MS-COCO2017 数据集上提高了 2.4 AP，在 PASCAL VOC2012 数据集上提高了 3.4 mAP，在 PASCAL VOC07+12 数据集上提高了 2.7 mAP。广泛的实验表明，我们的 RFENet 可以在不同的数据集上表现出色。该代码可在 https://github.com/object9detection/RFENet 获取。