融合非对称卷积的遥感图像目标检测算法

遥感图像中的目标具有背景复杂、方向多变等特点。利用传统方法进行遥感图像目标检测过程复杂且费时,存在精度低,漏检率高等问题。针对以上问题,提出一种改进的YOLOv5-AC算法,该算法以YOLOv5s模型为基础,首先在原有的Backbone中构建非对称卷积结构,增强模型对翻转和旋转目标的鲁棒性;其次在主干网络的C3模块中引入坐标注意力机制提升特征提取能力,并使用Acon自适应激活函数激活;最后使用CIOU作为定位损失函数以提升模型定位精度。改进后的YOLOv5-AC模型在NWPU VHR-10和RSOD数据集上进行实验,平均精确度均值分别达到了94.0%和94.5%,分别比原版YOLOv5s提升了1.8%和2.3%,有效提高了遥感图像目标检测精确度。

Object detection algorithm of remote sensing image based on asymmetric convolution

The object of remote sensing image has the characteristics of complex background and changeable direction. The process of object detection in remote sensing image using traditional methods is complex and time-consuming, with low accuracy and high rate of missed detection. To solve the above problems, we propose an improved YOLOv5 AC algorithm. This algorithm bases on the YOLOv5s model. First, an asymmetric convolution structure is built in the original Backbone to enhance the robustness of the model to flipped and rotated targets; Secondly, coordinate attention mechanism is introduced into C3 module of backbone network to improve feature extraction capability, and Acon (Activate Or Not) adaptive activation function is used for activation; Finally, we use CIOU as the location loss function to improve the positioning accuracy of the model. The improved YOLOv5-AC model was tested on NWPU VHR-10 and RSOD datasets, and the average accuracy reached 94.0% and 94.5%, respectively, 1.8% and 2.3% higher than the original YOLOv5s, which effectively improved the object detection accuracy of remote sensing images.