高阶深度可分离无人机图像小目标检测算法

当前无人机图像中存在小目标数量众多、背景复杂的特点, 目标检测中易造成漏检误检率较高的问题, 针对这些问题, 提出一种高阶深度可分离无人机图像小目标检测算法. 首先, 结合CSPNet结构与ConvMixer网络, 深度可分离卷积核, 获取梯度结合信息, 并引入递归门控卷积C3模块, 提升模型的高阶空间交互能力, 增强网络对小目标的敏感度; 其次, 检测头采用两个头部进行解耦, 分别输出特征图分类和位置信息, 加快模型收敛速度; 最后, 使用边框损失函数EIoU, 提高检测框精准度. 在VisDrone2019数据集上的实验结果表明, 该模型检测精度达到了35.1%, 模型漏检率和误检率有明显下降, 能够有效地应用于无人机图像小目标检测任务. 在DOTA 1.0数据集和HRSID数据集上进行模型泛化能力测试, 实验结果表明, 该模型具有良好的鲁棒性.

Small Target Detection Algorithm for High-order Depth Separable UAV Images

At present, there are many small targets in UAV images and the background is complex, which makes it easy to cause a high error detection rate in target detection. To solve these problems, this study proposes a small target detection algorithm for high-order depth separable UAV images. Firstly, by combining the CSPNet structure and ConvMixer network, the study utilizes the deeply separable convolution kernel to obtain the gradient binding information and introduces a recursively gated convolution C3 module to improve the higher-order spatial interaction ability of the model and enhance the sensitivity of the network to small targets. Secondly, the detection head adopts two heads to decouple and respectively outputs the feature map classification and position information, accelerating the model convergence speed. Finally, the border loss function EIoU is leveraged to improve the accuracy of the detection frame. The experimental results on the VisDrone2019 data set show that the detection accuracy of the model reaches 35.1%, and the missing and false detection rates of the model are significantly reduced, which can be effectively applied to the small target detection task of UAV images. The model generalization ability is tested on the DOTA 1.0 dataset and the HRSID dataset, and the experimental results show that the model has good robustness.