为提高SSD目标检测算法的小目标检测能力, 提出在SSD算法中引入转置卷积结构, 采用转置卷积将低分辨率高语义信息特征图与高分辨率低语义信息特征图相融合, 增加低层特征提取能力, 提高SSD算法的平均精准度。同时针对SSD算法存在模型过大, 运行内存占用量过高, 无法在嵌入式ARM设备上运行的问题, 以DenseNet为基础, 结合深度可分离卷积,逐点分组卷积与通道重排提出轻量化特征提取最小单元, 将SSD算法特征提取部分替换为轻量化特征提取最小单元的组合后, 可在嵌入式ARM设备上运行。在PASCAL VOC数据集和KITTI自动驾驶数据集上进行对比实验, 结果表明改进后的网络结构在平均精准度上得到明显提升, 模型参数数量得到有效降低。

In order to improve the small object detection ability of SSD object detection algorithm, the transposed convolution structure in SSD algorithm was proposed, the low resolution high semantic information feature map was integrated with high resolution low semantic information feature map using transposed convolution, which increased the ability of low level feature extraction and improved the average accuracy of SSD algorithm. At the same time for the problem that SSD algorithm model being large, running memory consumption high, without running on the embedded equipment ARM, a lightweight feature extraction minimum unit was proposed based on DenseNet, combining depthwise separable convolutions, pointwise group convolution and channel shuffle, running on the embedded equipment ARM cloud be realized. The comparative experiments on PASCAL VOC data set and KITTI autopilot data set show that the mean average is significantly improved by improved network structure, and the number of model parameters is effectively reduced.