基于深度学习的航空对地小目标检测

针对航拍图像中对地小目标识别率低、定位效果差的问题,提出了一种基于深度学习的目标检测算法。该算法利用VGG16网络作为微调网络,并添加部分深层网络,通过提取目标浅层特征与深层特征进行联合训练,克服检测过程中定位与识别相互矛盾的问题。提出把奇异值分解技术应用于卷积特征压缩处理,降低模型的计算与存储需求,并且采用多尺度训练方法以适应航空目标尺度的变化。实验结果表明,在通用数据集PASCAL上可以实现0.76mAP,检测速度达16fps,在专用航空目标数据集UCAS-AOD上可以实现0.63mAP,检测速度达18fps。基本满足对小目标检测精确度的要求,并且检测速度可以接近实时检测效果。

Detection of small target in aerial photography based on deep learning

In order to solve the problem of low recognition rate and poor positioning in aerial images, a target detection method based on deep learning is proposed. This algorithm uses VGG16 network as a fine tuning network and adds some deep network in it. Joint training is carried out by extracting the features of the shallow layers and the deep features of the target to overcome the contradiction between location and recognition in the process of detection. The singular value decomposition technology is used to compress the convolution features to reduce the computing and storage requirements of the model, and Multi scale training method is adopted to adapt to the change of aerial target scale. The experimental results show that 0.76 mAP can be implemented on the general data set PASCAL, and the detection speed is 16 fps. The 0.63 mAP can be achieved on the special aviation target data set UCAS-AOD, and the detection speed is 18 fps. It can satisfy the requirements for small target detection accuracy, and the detection speed can be close to the real-time detection effect.