注意力特征融合SSD算法对遥感图像的目标检测

Target detection of remote sensing image based on attention feature fusion SSD algorithm

针对多尺度单发射击检测(Single Shot MultiBox Detector,SSD)算法对小目标物体检测效果不佳的问题,提出注意力特征融合SSD(Attention Feature Fusion SSD,AFF-SSD)算法。首先,为了提升网络对小目标物体的检测性能,使用注意力特征融合模块对浅层特征图中的特征信息融合,在降低噪声的同时增强特征图中远距离像素的相关性;其次,针对训练过程中正负样本失衡导致的模型退化问题,结合聚焦分类损失函数对SSD算法中的损失函数优化;最后,引入迁移学习解决因训练数据较少导致的过拟合问题。实验结果表明,与SSD算法相比,AFF-SSD算法平均准确率均值提高8.09%,经过迁移学习后,AFF-SSD算法平均准确率均值提高3.47%。

Aiming at the problem that the single shot multibox detector( SSD) algorithm is not effective for small target object detection, an attention feature fusion SSD( AFF-SSD) algorithm is proposed. Firstly, in order to improve the detec-tion performance of the network for small target objects, the attention feature fusion module is used to fuse the feature information in the shallow feature map, which reduces the noise and enhances the correlation of distant pixels in the feature map. Secondly, for the model degradation caused by the imbalance between positive and negative samples in the training process, combined with the focus classification loss function, the loss function in the SSD algorithm is optimized.Finally, transfer learning is introduced to solve the problem of overfitting caused by less training data. The experimental results show that the average accuracy of the AFF-SSD algorithm is increased by 8.09% compared with the SSD algorithm.After the migration, the average accuracy of the AFF-SSD algorithm is increased by 3. 47 %.