Vehicle-mounted barrier detection method based on radar data and image data fusion and deep learning

A technology of obstacle detection and deep learning, which is applied in the directions of measuring devices, electromagnetic wave reradiation, character and pattern recognition, etc., can solve the problem that it is difficult to adapt to high-speed motion and bump imaging requirements, optical camera imaging does not have distance information, and data information is low Confidence and other issues, to achieve the effect of accelerating network parameter adjustment towards better results, improving vehicle endurance, and accelerating hardware power consumption

Active Publication Date: 2018-06-29
BEIHANG UNIV
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Problems solved by technology

However, optical camera imaging does not have distance information, and generally requires complex calculations to obtain inter-frame information based on algorithms, and the accuracy rapidly decays with the increase of the actual target distance
The lack of distance information limits the application of camera data to autonomous driving sc

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  • Vehicle-mounted barrier detection method based on radar data and image data fusion and deep learning
  • Vehicle-mounted barrier detection method based on radar data and image data fusion and deep learning
  • Vehicle-mounted barrier detection method based on radar data and image data fusion and deep learning

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Embodiment Construction

[0033] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0034] The present invention is a deep learning vehicle-mounted obstacle detection method based on radar and image data fusion, which is mainly used for vehicle-mounted sensor data fusion and target perception detection in the field of automatic driving, and the corresponding depth is obtained by preprocessing the calibrated radar data The image and height map data are fused with the RGB color image captured by the camera to train the Yolo convolutional network model. In real-time detection, the fusion data of radar and camera is directly input into the trained model, and the model gives the target position and related distance information in the picture.

[0035] The deep learning vehicle-mounted obstacle detection method based on radar and image data fusion of the present invention, the main steps are as follows image 3 As shown, the following wil...

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Abstract

The invention discloses a target detection algorithm based on intelligent equipment sensor data fusion and deep learning. Through fused radar point cloud data and camera data, data feature types capable of being sensed by a detection model are enriched. By performing model training on data channel fusion with different configuration, optimal channel configuration is selected, detection accuracy isimproved, and meanwhile calculation power consumption is lowered. By performing a test on real data, the channel configuration suitable for a real condition is determined, and the goals of processingfusion data by use of a Yolo deep convolutional neural network model and performing target barrier detection on a road scene is achieved.

Description

technical field [0001] The invention relates to a deep learning obstacle detection method based on radar and image data fusion, and belongs to the technical fields of sensor fusion, artificial intelligence and automatic driving. Background technique [0002] Self-driving vehicles perceive the vehicle's own operating conditions, road environment, and surrounding obstacles through a variety of sensors assembled around the body; through algorithms such as target recognition, lane detection, traffic signal recognition, and feasible area detection, the environment is recognized. Generate driving control commands, which not only drive the modified steering, acceleration and braking actuators to complete the self-driving process, the whole process includes: "perception-cognition-decision-making" dynamic environment interaction closed loop. [0003] Common Lidars for self-driving vehicles include iBeo and velodyne forward radars (4 and 8 lines) and 360-degree surround radars (16 lin...

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Application Information

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IPC IPC(8): G06K9/00G01S17/02G01S17/93
CPCG01S17/86G01S17/931G06V20/58
Inventor 牛建伟齐之平欧阳真超
Owner BEIHANG UNIV
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