YOLOv8作为一种高效的目标检测方法,可以快速,精确地识别图像中的物体。然而,传统算法在检测遥感图像中的小目标时遇到困难,例如缺少信息,背景噪声,以及复杂场景中多个物体之间的相互作用,这可能会影响性能。为了应对这些挑战,我们提出了一种优化的增强算法,用于检测遥感图像中的小目标,名为HP-YOLOv8。首先,我们设计了C2f-D-Mixer(C2f-DM)模块来代替原来的C2f模块。该模块集成了本地和全球信息,显著提高了检测小物体特征的能力。其次,我们介绍了一种基于注意力机制的特征融合技术,命名为门控特征金字塔网络(BGFPN)中的双层路由注意。该技术利用高效的特征聚合网络和重新参数化技术来优化不同比例尺特征图之间的信息交互,并通过双层路由注意(BRA)机制,它有效地捕获小物体的关键特征信息。最后,我们提出了形状平均垂直距离在联上相交(SMPDIoU)损失函数。该方法综合考虑了检测盒的形状和尺寸,增强了模型对检测框属性的关注,并提供了一种更为精确的边界框回归损失计算方法。为了证明我们的方法的有效性,我们在整个RSOD中进行了全面的实验,NWPUVHR-10和VisDrone2019数据集。实验结果表明,HP-YOLOv8达到95.11%,93.05%,在mAP@0.5度量中,为53.49%,72.03%,65.37%,在更严格的mAP@0.5:0.95公制中,38.91%,分别。
YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-
YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-Level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small objects. Finally, we propose the Shape Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model\'s focus on the attributes of detection boxes, and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach\'s efficacy, we conducted comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-
YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.