基于深度学习的多目标车辆检测及追踪方法

Method of Multi-target Vehicle Detection and Tracking Based on Deep Learning

文章是以MTALAB软件为主要平台,基于深度学习建立一种多目标车辆检测及追踪的方法。首先建立一个基于深度学习的模型用于训练的不同场景的车辆数据集,并对所采集的数据集进行标注和格式归一化处理。然后使用K-means聚类算法进行锚框,建立以YOLOv3SPP算法为主的神经网络框架,采用非极大值拟制(NMS)算法得到最终的预测框。最终训练神经网络模型,再对该模型进行测试和评定。经实验可以得出该模型能够准确地检测及追踪多目标车辆。

This paper uses MTALAB software as the main platform,and establishes a multi-target vehicle detection and tracking method based on deep learning.Firstly,a deep learning-based model is established for training vehicle datasets of different scenarios,and the datasets are labeled and format normalized.Then uses the K-means clustering algorithm to anchor the frame,establishes a neural network framework based on the YOLOv3 SPP algorithm,and uses the non-maximum suppression(NMS)algorithm to obtain the final prediction frame.At last,the neural network model is trained.Meanwhile,it is tested and evaluated.The study shows that this model can accurately detect and track multi-target vehicles.