UAV vehicle detection based on convolutional neural network exits a key problem of information imbalance of different feature layers. Shallow features have spatial information that is beneficial to localization, but lack semantic information. On the contrary, deep features have semantic information that is beneficial to classification, but lack spatial information. However, accurate classification and localization for UAV vehicle detection require both shallow spatial information and high semantic information. In our work, a bi-directional information guidance network (BDIG-Net) for UAV vehicle detection is proposed, which can ensure that each feature prediction layer has abundant mid-/low-level spatial information and high-level semantic information. There are two main parts in the BDIG-Net: shallow-level spatial information guidance part and deep-level semantic information guidance part. In the shallow-level guidance part, we design a feature transform module (FTM) to supply the mid-/low-level feature information, which can guide the BDIG-Net to enhance detailed and spatial features for deep features. Furthermore, we adopt a light-weight attention module (LAM) to reduce unnecessary shallow background information, making the network more focused on small-sized vehicles. In the deep-level guidance part, we use classical feature pyramid network to supply high-level semantic information, which can guide the BDIG-Net to enhance contextual information for shallow features. Meanwhile, we design a feature enhancement module (FEM) to suppress redundant features and improve the discriminability of vehicles. The proposed BDIG-Net can reduce the information imbalance. The experimental results show that the BDIG-Net can achieve accurate classification and localization for UAV vehicles and realize the real-time application requirements.

基于卷积神经网络的无人机车辆检测存在不同特征层信息不平衡的关键问题。浅层特征具有有利于定位的空间信息，但缺乏语义信息。相反，深层特征具有有利于分类的语义信息，但缺乏空间信息。然而，无人机车辆检测的准确分类和定位需要浅层空间信息和高语义信息。在我们的工作中，提出了一种用于无人机车辆检测的双向信息引导网络（BDIG-Net），它可以确保每个特征预测层具有丰富的中/低层空间信息和高层语义信息。 BDIG-Net主要有两个部分：浅层空间信息引导部分和深层语义信息引导部分。在浅层指导部分，我们设计了一个特征转换模块（FTM）来提供中/低层特征信息，它可以指导BDIG-Net增强深层特征的细节和空间特征。此外，我们采用轻量级注意力模块（LAM）来减少不必要的浅层背景信息，使网络更加关注小型车辆。在深层指导部分，我们使用经典的特征金字塔网络来提供高层语义信息，这可以指导BDIG-Net增强浅层特征的上下文信息。同时，我们设计了特征增强模块（FEM）来抑制冗余特征并提高车辆的可辨别性。所提出的BDIG-Net可以减少信息不平衡。实验结果表明，BDIG-Net能够实现对无人机车辆的准确分类和定位，实现实时应用需求。