基于改进YOLOv5s算法的遥感图像飞机检测

Remote sensing image aircraft detection based on improved YOLOv5s algorithm

针对遥感飞机图像中存在背景复杂、密集小目标及目标遮挡等导致飞机检测精度不高的问题,提出一种基于改进YOLOv5s的遥感图像飞机检测算法。采用稠密连接增强特征传播与特征重用,减轻训练过程中的梯度消失问题;引入注意力机制进行自适应特征细化,提升密集小飞机目标的检测性能;改进损失函数增强目标被遮挡或多目标重叠情况下的检测效果。实验结果表明,改进后的算法能显著提高遥感图像飞机检测的精度,具有较强的实用性与适用性。

Aiming at the problem of low accuracy of remote sensing image aircraft detection due to complex background,dense small targets and occluded targets,a remote sensing image aircraft detection algorithm based on improved YOLOv5s was proposed.The dense connections were used to enhance feature propagation and feature reuse,so as to alleviate the problem of gradient disappearance in the training process.The attentional mechanism was introduced for adaptive feature refinement to improve the detection performance of dense small targets detection.The loss function was improved to enhance the detection effect under occlusion and multi-target overlap.According to the experimental results,the optimized algorithm can availably improves the accuracy of remote sensing image aircraft detection,and has strong practicability and applicability.