Research On Pedestrian Detection Technology Based On Deep Learning

Pedestrian detection technology is to obtain the video image information under the traffic scene or monitoring video through the camera,so as to achieve the accurate positioning of pedestrian target.The traditional pedestrian detection algorithm mostly relies on the artificial design features,which is vulnerable to the influence of environmental factors and human factors and can not achieve the ideal effect.Because of its excellent feature learning ability,deep learning has made a great breakthrough in the aspect of object detection.However,pedestrian detection in complex scenes is extremely challenging due to the influence of diverse human posture,different clothing and changes in light.Based on SSD(Single Shot Multibox Detector),the deep learning target detection algorithm was improved in this paper to improve the performance of pedestrian target detection.The specific work is as follows:1.Aiming at the problems such as the weak correlation between the volume layers of SSD network,the low resolution of the features sent to the detection layer and the inaccuracy of target location,a network based on feature fusion and additional feature extraction is proposed.The basic network part of SSD network is improved,and the shallow features and deep features are fused,so that the context information of the network can be fully utilized and the positioning ability of the network to the pedestrian target can be enhanced.The residual blocks are added before the feature maps enters the detection layer,which can reduce the calculation and increase the ability of feature expression.The experiment shows that feature fusion and the addition of residual blocks can reduce the rate of missing and false detection of pedestrian detection to a certain extent.2.In view of the insufficient shallow feature information extraction and semantic information of feature map in the original SSD algorithm,an attention mechanism based SSD pedestrian detection algorithm is proposed to enhance the information related to pedestrian detection tasks and suppress the irrelevant information,so as to enhance the pedestrian characteristics.SSD-SENet network is obtained by adding channel attention module to the original basic network VGG-16 and add channel and spatial attention module to the original basic network VGG-16 called SSD-CBAM network.Experimental results show that the SSD pedestrian detection algorithm with attention mechanism introduced in this paper have lower miss rate and false rate than the original algorithm.