A New Method for Pedestrian Detection with Lightweight Backbone based on Yolov3 Network

• 作者: Qirui Dong
• 来源期刊: 电子研究与应用
• 学科: 工学
• 关键词: PEDESTRIAN detection Convolutional Neural NETWORK AUTONOMOUS driving algorithms DARKNET LIGHTWEIGHT framework
• 年，卷（期）: 2019,（5）
• 页码范围: 5-6
• 页数: 2页
• 分类号: TP3

摘要

The main purpose of YOLOv3,aiming to improve the detection speed and accuracy from current detection models,is to predict the center coordinates of(x,y)from the Bounding Box and its length,width through multiple layers of VGG Convolutional Neural Network(VGG-CNN)and uses the Darknet lightweight framework to process images at a faster speed.More specifically,our model has been reduced part of YOLOv3's complex and computationally intensive procedures and improved its algorithms to maintain the efficiency and accuracy of object detection.By this method,it performs a higher quality on mass object detection tasks with fewer detection errors.