Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (17): 93-100.DOI: 10.3778/j.issn.1002-8331.2109-0290

• Target Detection • Previous Articles     Next Articles

Remote Sensing Image Target Detection Algorithm Based on Residual Shrinkage Network

GAO Ye, GUO Songyi, SHE Xiangyang   

  1. College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China
  • Online:2022-09-01 Published:2022-09-01



  1. 西安科技大学 计算机科学与技术学院,西安 710054

Abstract: Aiming at the problems of complex background,more noise,dense arrangement of small targets and large difference of target scale in remote sensing images,a remote sensing image target detection algorithm based on improved channel attention and residual shrinkage network is proposed in this paper. The algorithm uses convolutional neural network,takes YOLOV3 model as the basic network,selects mosaic image enhancement for data preprocessing,reconstructs the feature extraction network by using the depth residual shrinkage module,constructs the spatial pyramid pooling fusion layer by combining the channel attention mechanism and combination pooling,calculates the positioning loss by using CIOU,and finally realizes the target detection of remote sensing image. Experimental results show that compared with the original algorithm,the overall map of the improved algorithm is improved from 89.2% to 92.2%,and better performance is obtained.

Key words: channel attention, feature fusion, remote sensing image, residual shrinkage network

摘要: 针对于遥感图像中背景复杂噪声多、小目标多且排布密集、目标尺度差异大等问题,提出了一种改进通道注意力与残差收缩网络的遥感图像目标检测算法。该算法借助卷积神经网络,以YOLOV3模型作为基础网络,选择Mosaic图像增强的方式进行数据预处理,采用深度残差收缩模块重构了特征提取网络,并结合通道注意力机制与组合池化构建空间金字塔池化融合层,采用CIOU进行定位损失计算,最终实现遥感图像目标检测。实验结果表明:改进算法相比于原算法的总体mAP由89.2%提升至92.2%,获得了更好的性能表现。

关键词: 通道注意力, 特征融合, 遥感图像, 残差收缩网络