Pedestrian Detection Based on Improved SSD Object Detection Algorithm

Abstract

Pedestrian detection is an important application of object detection. SSD is one of the target detection algorithms based on deep learning with better performance. The weak detection ability of SSD for small objects, and there will still be false detections and missed detections in the detection situation of the complex environment. In order to improve the detection accuracy of SSD for pedestrians, we propose an improved SSD object detection algorithm based on DenseNet and multi-scale feature fusion. Based on the SSD algorithm, we design the DenseNet-66 module to enhance the feature extraction and utilization capabilities of the model. In the target detection part, a fusion mechanism of multi-scale feature layers is introduced, and an attention feature fusion module is added to further improve the detection performance of the model for small target pedestrians. After training on PASCAL VOC, INRIA, ETH, TUD, CoCo datasets, the experimental results show that our improved SSD model has 300 × 300 input to achieve PASCAL VOC 2007, VOC 2012, INRIA, ETH, TUD, CoCo datasets Up 89.50% mAP, 84.76% mAP, 78.49% mAP, 69.50% mAP, 78.58% mAP and 57.35% mAP. Compared with SSD, the improved SSD detection accuracy increases by 3.75%, 1.77%, 3.06%, 3.66%, 1.90% and 1.87%, respectively.