论文标题
多光谱融合用于对象检测的循环保险丝和refine块
Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
论文作者
论文摘要
当在不同环境中检测具有相同模型的对象时，多光谱图像（例如可见和红外线）可能特别有用。为了有效地使用不同的光谱，主要技术问题位于信息融合过程中。在本文中，我们为神经网络提出了一种新的中途功能融合方法，该方法通过添加到网络体系结构中，利用了多光谱特征中存在的互补/一致性平衡，这是一个周期性融合并完善每个光谱功能的特定模块。我们评估了融合方法对两个具有挑战性的多光谱数据集以进行对象检测的有效性。我们的结果表明，与其他最先进的多光谱对象检测方法相比，在任何网络中实现我们的环状保险丝和refine模块都可以改善两个数据集的性能。
Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a new halfway feature fusion method for neural networks that leverages the complementary/consistency balance existing in multispectral features by adding to the network architecture, a particular module that cyclically fuses and refines each spectral feature. We evaluate the effectiveness of our fusion method on two challenging multispectral datasets for object detection. Our results show that implementing our Cyclic Fuse-and-Refine module in any network improves the performance on both datasets compared to other state-of-the-art multispectral object detection methods.
