基于倒置残差注意力的无人机航拍图像小目标检测

针对无人机航拍图像背景复杂、小尺寸目标较多等问题，提出了一种基于倒置残差注意力的无人机航拍图像小目标检测算法。在主干网络部分嵌入倒置残差模块与倒置残差注意力模块，利用低维向高维的特征信息映射，获得丰富的小目标空间信息和深层语义信息，提升小目标的检测精度；在特征融合部分设计多尺度特征融合模块，融合浅层空间信息和深层语义信息，并生成4个不同感受野的检测头，提升模型对小尺寸目标的识别能力，减少小目标的漏检；设计马赛克混合数据增强方法，建立数据之间的线性关系，增加图像背景复杂度，提升算法的鲁棒性。在VisDrone数据集上的实验结果表明：所提模型的平均精度均值比DSHNet模型提升了1.2%，有效改善了无人机航拍图像小目标漏检、误检的问题。

Small object detection in UAV aerial images based on inverted residual attention

Aiming at the problems of complex background and too many small-size targets in UAV aerial images, a small target detection algorithm based on inverted residual attention is proposed. Firstly, an inverted residual module and an inverted residual attention module are embedded into the backbone network, while rich spatial information and deep semantic information of small targets are obtained by feature information mapping from low dimension to high dimension, thus improving the accuracy of small target detection; Secondly, in feature fusion, a multi-scale feature fusion module is established to fuse the shallow spatial information and deep semantic information, and to generate four detection heads with different sensory fields, which improves the recognition of small-size targets and reduces missed detection of small targets; Finally, a mosaic mixed data enhancement method is designed to establish the linear relationship between the data, increase the complexity of the image background and improve the robustness of the algorithm. The experimental results on data set VisDrone show that the mean average precision of this algorithm is 1.2% higher than that of DSHNet, which means that the proposed algorithm could effectively reduce missed detection and false detection of small targets in UAV aerial images.