基于改进级联算法的遥感图像目标检测

Target Detection in Remote Sensing Images Based on Improved Cascade Algorithm

针对遥感图像中小目标规模大、分布不均匀、尺度比例变化大和背景复杂等问题,提出一种改进的级联算法SACascade。该算法使用循环特征金字塔使产生的特征表示逐步增强,提高小目标的检测率。使用基于可学习锚的建议区域生成网络,对遥感目标进行精确定位,并且引入特征自适应模块和特征融合模块,以提高模型对复杂背景图像的检测性能。在级联的基础上引入双分支检测头以提高模型对小目标的检测性能。在TGRS-HRRSD-Dataset和VisDroneDET数据集上对不同算法进行对比实验,实验结果表明:改进后的级联算法可以更精确地对遥感图像目标进行检测和定位,相比改进前的级联算法在两个数据集上的精度分别提高2.94%和9.71%。

Given the large scale, uneven distribution, and large scale changes of small targets and the complex background in remote sensing images, an improved cascade algorithm SA-Cascade is proposed. This algorithm uses a recurrent feature pyramid network to strengthen the feature representation generated step by step, thereby improving the detection rate of small targets. The region proposal generation network based on the learnable anchor is utilized to locate the remote sensing target accurately. The feature adaptation module and feature fusion module are introduced to improve the performance in detecting images with complex backgrounds. On the basis of the cascade algorithm, the two-branch detection head is adopted to improve the performance of the model for detecting small targets. A comparative experiment of various algorithms is performed on TGRS-HRRSD-Dataset and VisDrone-DET dataset. The experimental results show that the improved cascade algorithm can detect and locate remote sensing image targets more accurately. Compared with the original cascade algorithm, the improved one increases the accuracy on the two datasets by 2. 94% and 9. 71%,respectively.