改进YOLOv7网络在低空遥感图像目标检测中的应用

Low-altitude remote sensing image object detection based on improved YOLOv7 network

针对低空遥感图像目标检测存在的尺度微小、背景复杂多变和计算资源有限等问题,提出了一种改进YOLOv7网络的低空遥感图像目标检测网络SimAM_YOLOv7。首先,基于张量火车分解,最小化冗余参数;其次,引入无参数的注意力机制,提高网络对目标的聚焦能力;最后,利用高效IoU(EIoU)优化定位损失,减小目标框与先验框的位置偏移,基于Focal Loss改进分类损失,解决正负样本的失衡问题。在真实低空遥感数据集上进行实验,在YOLOv7的基准下,所提出的网络在参数量减少3.27M时,mAP 50指标提高了4.63%,mAP 50:95指标提高了3.94%,充分验证了所提网络的有效性和优越性。

To address the bottlenecks caused by issues such as small scales,complex and variable backgrounds,and limited computing resources in low-altitude remote sensing image object detection,a new low-altitude remote sensing image object detection method,named SimAM_YOLOv7,is proposed,based on improved YOLOv7 network.Firstly,based on tensor train decomposition,redundant parameters are minimized.Secondly,a non-parametric attention module is introduced to enhance the network's ability to focus on targets.Then,an efficient intersection over union(EIoU)is utilized to optimize the positioning loss,reducing the positional offset between the target box and the prior box.Furthermore,the classification loss is improved based on Focal Loss to overcome the imbalance between positive and negative samples.Experiments conducted on a real-world low-altitude remote sensing dataset demonstrate that,compared to the YOLOv7 baseline,the proposed method increases mAP 50 by 4.63%and increases mAP 50:95 by 3.94%while the number of parameters is reduced by 3.27M,fully validating its effectiveness and superiority.