基于位置感知和跨层特征融合的航拍小目标检测算法

Small target detection algorithm for aerial photography based on location awareness and cross-layer feature fusion

针对航拍图像目标尺度小、背景复杂、漏检和误检严重,提出了一种基于位置感知和跨层特征融合的航拍小目标检测算法DC-YOLOv8s。DC-YOLOv8s新增小目标检测层,增强对小目标尺度的敏感性,提高检测精度。为了减少特征信息的丢失,设计了跨层特征融合模块,充分融合小目标浅层语义信息和深层语义信息,丰富特征表示。改进C 2f结构,结合可变性卷积设计了基于位置感知融入残差的感受野注意力模块,适应航拍小目标形状的变化,快速提取感受野特征,降低漏检和误检率。最后使用基于注意力机制的动态检测头在尺度感知、空间感知、任务感知方面提高复杂场景下小目标的定位性能。实验表明,在VisDrone2019数据集上,DC-YOLOv8s在P、R、mAP上相较于YOLOv8s分别提高了7.2%、7.5%、9.1%,显著提高了小目标检测的性能,FPS为71帧,满足实时性要求。在VOC2007+2012上进行模型泛化性实验验证,效果优于其他经典算法。

Aiming at the small target scale,complex background,and serious leakage and misdetection of aerial images,an aerial small target detection algorithm based on location awareness and cross-layer feature fusion,DC-YOLOv8s,is proposed.DC-YOLOv8s adds a new small target detection layer,which enhances the sensitivity to the small target scale and improves the detection accuracy.In order to reduce the loss of feature information,a cross-layer feature fusion module is designed to fully fuse the small target shallow semantic information and deep semantic information to enrich the feature representation.Improve the C2f structure,combined with variability convolution to design a sensory field attention module based on position-aware incorporation of residuals,adapting to the changes in the shape of aerial small targets,quickly extracting sensory field features,and reducing the rate of leakage detection and false detection.Finally,the dynamic detection head based on the attention mechanism is used to improve the localization performance of small targets in complex scenes in terms of scale perception,spatial perception,and task perception.The experiments show that on VisDrone2019 dataset,DC-YOLOv8s improves 7.2%,7.5%,and 9.1%on P,R,and mAP respectively compared to YOLOv8s,which significantly improves the performance of small target detection,and the FPS is 71 frames,which meets the real-time requirement.Experimental verification of model generalizability is carried out on VOC2007+2012,and the effect is better than other classical algorithms.