感受野特征增强的SSD目标检测算法

SSD Object Detection Algorithm with Feature Enhancement of Receptive Field

SSD(Single Shot multi-box Detector)算法是在不同层的特征图上,进行多尺度对象的检测,具有速度快和精度高的特点.但是,传统SSD算法的特征金字塔检测方法很难融合不同尺度的特征,并且由于底层的卷积神经网络层具有较弱的语义信息,也不利于小物体的识别,因此本论文提出了以SSD算法的网络结构为基础的一种新颖的目标检测算法RF_SSD,该算法将不同层及不同尺度的特征图以轻量级的方式相融合,下采样层生成新的特征图,通过引入感受野模块,提高网络的特征提取能力,增强特征的表征能力和鲁棒性.和传统SSD算法相比,本文算法在精度上有明显提升,同时充分保证了目标检测的实时性.实验结果表明,在PASCAL VOC测试集上测试,准确率为80.2%,检测速度为44.5 FPS.

SSD(Single Shot multi-box Detector)algorithm is used to detect multi-scale objects on feature maps of different layers,which has the characteristics of fast speed and high accuracy.However,the feature pyramid detection method of traditional SSD algorithm is difficult to fuse the features of different scales,and because the convolutional neural network layer at the bottom has weak semantic information and is not conducive to the recognition of small objects,so this paper proposes a novel object detection algorithm RF_SSD based on the network structure of SSD algorithm.In this algorithm,feature maps of different layers and scales are fused in a lightweight way,and new feature maps are generated in the lower sampling layer.By introducing the receptive field module,the feature extraction ability of the network is improved,and the characterization ability and robustness of the feature are enhanced.Compared with the traditional SSD algorithm,the accuracy of the proposed algorithm is significantly improved,and the real-time performance of object detection is fully guaranteed.The experimental results show that the accuracy is 80.2%and the detection speed is 44.5 FPS on the PASCAL VOC test set.