基于改进SSD算法的航拍目标检测算法研究

研究基于深度学习技术的无人机航拍图像目标检测算法，首先介绍目标检测算法SSD（Single Shot MultiBox Detector），并对其特征提取网络进行改进，采用稠密特征提取网络替换原网络的主干特征提取网络，提高算法的特征提取能力，从而提升了算法的检测精度。针对网络实时性问题，在算法中引入分组卷积，极大地减少了网络参数量，提升了网络推理速度。为解决训练中出现的正负样本不均衡问题，利用焦点损失（Focal Loss）改进了原算法的损失函数，进一步提升了网络的收敛速度和精度。最后，通过仿真验证了改进算法在目标检测精度上的优越性。

Research on aerial target detection algorithm based on improved SSD algorithm

This paper studies the problem of target detection in UAV aerial images using deep learning technology. Let''s start with single shot multibox detector target detection algorithm, and then improve it. Dense feature extraction network is used to improve the feature extraction ability of the algorithm, so as to improve the detection accuracy of the algorithm. Aiming at the problem of network real-time, packet convolution is introduced into the algorithm, which greatly reduces the amount of network parameters and improves the speed of network reasoning. In order to solve the positive and negative problems in training for the sample imbalance problem, this paper improves the loss function of the original algorithm and uses focal loss to improve the original loss function, which further improves the convergence speed and accuracy of the network. Finally, the superiority of the algorithm in target detection accuracy and speed is verified by simulation.