多层卷积特征的真实场景下行人检测研究

针对真实场景下的行人检测方法存在漏检、误检率高，以及小尺寸目标检测精度低等问题，提出了一种基于改进SSD网络的行人检测模型（PDIS）。PDIS通过引出更底层的输出特征图改进了原始SSD网络模型，并采用卷积神经网络不同层输出的抽象特征对行人目标分别做检测，融合多层检测结果，提升了小目标行人的检测性能。此外，针对数据集样本多样性能有效地提升检测算法的泛化能力，本文采集了不同光照、姿态、遮挡等复杂场景下的行人图像，对背景比较复杂的INRIA行人数据集进行了扩充，在扩增的行人数据集上训练的PDIS模型，提高了在真实场景下的行人检测精度。实验表明:PDIS在INRIA测试集上测试结果达到93.8%的准确率，漏检率低至7.4%。

Research on pedestrian detection based on multi-layer convolution feature in real scene

Pedestrian detection methods in real scenes face some problems due to the high miss detection and false detection as well as the low detection accuracy of small size objects. To solve these problems, a pedestrian detection model based on improved SSD (PDIS) is proposed. The PDIS method improves the original SSD network model by extracting the lower-level output feature maps. It employs the abstract features of different convolutional neural network layers to detect pedestrians respectively, and then integrates the detection results of multi layers to increase the pedestrian detection performance for small sizes. Considering that the diversity of dataset can effectively enhance the generalization ability of detection algorithm, the paper expands the INRIA pedestrian dataset with complex background by collecting pedestrian images with different illumination, pose and occlusion. The PDIS method trained on expanded pedestrian dataset increases the precision rate of pedestrian detection in real scenes. The experiment results on INRIA test set indicate that the precision rate of PDIS algorithm is up to 93.8% and the miss rate is as low as 7.4%.