基于特征融合与注意力机制的无人机图像小目标检测算法

Small object detection algorithm in UAV image based on feature fusion and attention mechanism

由于无人机航拍图像目标物体尺寸太小、包含的特征信息少,导致现有的检测算法对小目标的检测效果不理想。针对该问题,在YOLOv5主干网络中融入多头注意力机制,可以有效整合全局特征信息。随着网络深度的不断加深,模型将更关注高层的语义信息,进而忽略对小目标检测至关重要的底层细节纹理特征,以致小目标的检测效果较差。因此,提出浅层特征增强模块来学习底层特征信息,达到增强小目标特征信息的目的。此外,为了加强特征融合的能力,设计了一种多级特征融合模块,将不同层级的特征信息进行聚合,使网络能够动态调节各输出检测层的权重。实验结果表明,该算法在公开数据集VisDrone2021平均均值精度达到45.7%,相比原YOLOv5算法提升了3.1%,对高分辨率图像的检测速度FPS达到41帧/秒,满足实时性,与其他主流算法相比该算法检测精度有明显提升。

The task of detecting small objects in UAV aerial images is a formidable challenge due to their diminutive size and insufficient amount of feature information.To surmount this predicament,a multi-head attention mechanism was incorporated into the YOLOv5 backbone network in order to seamlessly integrate global feature information.As the network depth increased,the model tended to accentuate high-level semantic information at the expense of underlying detailed texture features vital for the detection of small objects.To address this issue,a shallow feature enhancement module was devised to acquire underlying feature information and augment small object feature information.Furthermore,a multi-level feature fusion module was developed to amalgamate feature information from different layers,thus enabling the network to dynamically adjust the weights of each output detection layer.Experimental results on the publicly available VisDrone2021 dataset demonstrated that the mean average precision of the proposed algorithm,attained a level of 45.7%,representing a 3.1%enhancement over the baseline YOLOv5 algorithm.Additionally,the proposed algorithm achieved a detection speed of 41 frames per second for high-resolution images,satisfying the requirement for real-time performance and exhibiting a noteworthy improvement in detection accuracy over other prevalent methods.