基于特征优化与深层次融合的目标检测算法

针对单阶段多边框检测算法(SSD)存在对小目标检测误差较大的问题，提出基于特征优化与深层次融合的目标检测算法，通过空间通道特征增强(SCFE)模块和深层次特征金字塔网络(DFPN)改进SSD. SCFE模块基于局部空间特征增强和全局通道特征增强机制优化特征层，注重特征层的细节信息；DFPN基于残差空间通道增强模块改进特征金字塔网络，使不同尺度特征层进行深层次特征融合，提升目标检测精度. 在训练阶段添加样本加权训练策略，使网络注重训练定位良好的样本和置信度高的样本. 实验结果表明，在PASCAL VOC数据集上，所提算法在保证速度的同时检测精度由SSD的77.2%提升至79.7%；在COCO数据集上，所提算法的检测精度由SSD的25.6%提升至30.1%，对小目标的检测精度由SSD的6.8%提升至13.3%.

Object detection algorithm based on feature enhancement and deep fusion

A object detection algorithm based on feature optimization and deep fusion was proposed, aiming at the problems of single-stage multi-box detector algorithm (SSD) with large detection errors for small targets. SSD was improved through spatial and channel feature enhancement (SCFE) and deep feature pyramid network (DFPN). A feature layer based on the local spatial feature enhancement and the global channel feature enhancement mechanism was optimized by SCFE?module which focused on detail information of the feature layer. Based on the residual space channel enhancement module, feature?pyramid?network was?improved by DFPN which fused feature layers of different scales and improved the accuracy of object detection. At the same time, a sample weighted training strategy was added in the training stage, which made the network focused on training samples with good position and high confidence. The experimental results show that on the PASCAL VOC dataset, the detection accuracy of the proposed algorithm is improved from 77.2% to 79.7% of SSD while ensuring speed. On the COCO dataset, the detection accuracy of the proposed algorithm is increased from 25.6% to 30.1% for that of SSD, and the detection accuracy for small targets is increased from 6.8% to 13.3% for that of SSD.