Pedestrian Detection method based on Multi-Scale Fusion Inception-SSD Model
Xin Li, Xiangao Luo, Haijiang Hao, Fan Chen, Mengting Li · 2020
Pedestrian detection is widely used in daily life. many fields require high accuracy and fast speed of pedestrian detection is an urgent problem to be solved. the depth, convolution kernel size, and feature layer selection of neural networks have a great impact on the performance of target detection. in this paper, based on Single Shot MultiBox Detector (SSD), a pedestrian detection method based on Inception-SSD of sparse connections and multi-scale fusion is proposed. this algorithm achieves good performance in both detection speed and detection accuracy. Through comparing the experimental data on the PASCAL VOC and CUHK Occlusion image data sets, it shows that some of the optimized designs adopted in this paper have higher accuracy than the original algorithm, and the detection speed reaches 31 fps to meet the real-time requirements.