基于R-D SSD模型航空发动机安装工位检测算法

为解决航空发动机在安装过程中大多实行人工安装、定位不精确等问题，在研究其自动化安装方法中，针对航空发动机安装工位的检测需求，提出了一种残差网络与膨胀卷积相融合的SSD改进算法(R-D SSD)。将经典SSD模型的主干网络VGG16替换为残差网络ResNet-101，并增加其输出特征图上的预选框数量，解决了原始算法对底层特征抓取能力不足的问题，进而弥补了对小目标检测效果较差的缺陷；利用膨胀卷积扩大网络的感受野，获取足够的安装工位边缘特征细节信息，在不改变网络结构的同时，保证了模型良好的实时性和对目标的检测精度。实验表明:对于小目标数据集和整个数据集，R-D SSD算法的平均检测精度较原始算法分别提高了8.6%和4.0%，可以满足航空发动机安装时平均检测精度不低于85%的要求。 

Detection algorithm of aeroengine installation station based on R-D SSD model

In order to solve the problems of manual installation and inaccurate positioning in the process of aeroengine installation, an improved SSD algorithm (ResNet-Dilated SSD, R-D SSD) is proposed to meet the detection requirements of aeroengine installation station in the research of its automatic installation method. The VGG16, the backbone network of classical SSD model, is replaced by the residual network ResNet-101 and the number of preselected boxes on output feature map is increased, which solves the problem that original algorithm has insufficient ability to grasp the underlying features, and thus results in poor detection effect of small target. The dilation convolution is used to expand the network's receptive field to obtain enough edge feature information of installation station, which ensures the good real-time performance of the model and the detection accuracy of the target without changing the network structure. The experimental results show that the average detection accuracy of the R-D SSD detection algorithm is 8.6% and 4.0% higher than that of original algorithm for the small target dataset and the whole dataset. It can meet the requirement that the average detection accuracy is not less than 85% when the aeroengine is installed.