Monocular vision road target detection and distance estimation method based on improved YOLOv3

A technology of target detection and distance estimation, applied in the field of image processing and computer vision, can solve the problems of single detection effect, inability to obtain sufficient information, inability to balance the accuracy rate of road targets and real-time performance, etc. Effect

Active Publication Date: 2020-07-28
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0008] The purpose of the present invention is to solve two problems, namely the problem that the accuracy and real-time performance of road target detection cannot be balanced and the problem that a single detection effect cannot obtain sufficient information

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  • Monocular vision road target detection and distance estimation method based on improved YOLOv3
  • Monocular vision road target detection and distance estimation method based on improved YOLOv3
  • Monocular vision road target detection and distance estimation method based on improved YOLOv3

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specific Embodiment approach

[0046] combine figure 1 , the specific implementation is as follows:

[0047] Step 1: Obtain a road object image dataset containing road object bounding boxes, category information and distance information, divide the data set into a training set and a verification set, and perform certain data preprocessing.

[0048] This step includes: making the label information such as the target bounding box, category and distance of the road target graphic dataset into Pascal VOC or MS COCO dataset format, and then dividing the road target image dataset into a training set and a verification set according to a certain ratio. In this embodiment, the road object image dataset focuses on three types of road objects (i.e., vehicles, cyclists, and pedestrians), and the road object image dataset is divided into a training dataset and a verification dataset according to a ratio of 9:1. .

[0049] Step 2: Build an improved YOLOv3 network structure based on the Global-Context structure and the...

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Abstract

The invention discloses a monocular vision road target detection and distance estimation method based on improved YOLOv3, belongs to the field of image processing and computer vision, and is suitablefor intelligent systems of aided driving, road environment perception and the like. The implementation process of the method comprises the following steps: acquiring a road target image marked with road target bounding box information, distance information and category information; constructing an improved YOLOv3 network model based on a Global-Context structure and a cavity convolution pooling pyramid structure; redesigning a loss function of the network in combination with a perspective projection relationship of a camera system and a frame prediction mechanism of YOLOv3; and training and obtaining a road target detection and distance estimation model, and realizing detection and distance estimation of the road target by utilizing the model. Through the monocular vision road target detection and distance estimation method, the distance of the road target can be estimated while the road target is quickly and accurately recognized and positioned in a natural scene, and the monocular vision road target detection and distance estimation method has important significance for practical application.

Description

technical field [0001] The invention relates to image processing and computer vision technology, in particular to a monocular vision road target detection and distance estimation method based on improved YOLOv3. Background technique [0002] At this stage, the government's increasing investment in transportation facilitation has led to the rapid development of expressways. More and more people are looking for convenient transportation and buying cars. The steady increase in the number of cars makes traffic accidents more and more likely to occur, which poses a serious threat To the safety of people's lives. In the past, researchers focused on the study of automotive passive safety technology, but this method can only reduce the damage to the driver through buffering after the car has been in an accident. Nowadays, with the development of autonomous driving technology, researchers have also shifted from research on passive safety technology to active safety technology. To r...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/56G06V2201/07G06N3/045G06F18/214Y02T10/40
Inventor 秦华标连国妃
Owner SOUTH CHINA UNIV OF TECH
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