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Pit detection method based on improved YOLOv3

A detection method and pothole technology, applied in the field of image recognition, to improve the detection accuracy, ensure the detection accuracy and speed, and increase the weight

Active Publication Date: 2021-10-08
CENT SOUTH UNIV
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Problems solved by technology

[0004] Based on the above problems, the present invention provides a pothole detection method based on improved YOLOv3 to solve the problem that the pothole detection must not only ensure real-time performance, but also further improve the accuracy rate

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  • Pit detection method based on improved YOLOv3
  • Pit detection method based on improved YOLOv3
  • Pit detection method based on improved YOLOv3

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Embodiment Construction

[0058] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0059] The present invention is a pothole detection method based on improved YOLOv3, such as figure 1 shown, including the following steps:

[0060] S1. Collect pothole pictures through the visual acquisition system, and obtain a pothole data set after preprocessing, and the pothole data set includes preprocessed pothole images;

[0061] S2. Building an improved YOLOv3 pothole detection network model

[0062] S2.1. Construct feature extraction network my_Darknet-101: use the Get_Feature feature extraction module to extract the edge and texture information of potholes from the pothole dataset as the initial module, and use 3 densely connected blocks Pothole_Block as the b...

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Abstract

The invention discloses a pit detection method based on improved YOLOv3, and the method comprises the steps: S1, collecting a pit image through a visual collection system, and obtaining a pit data set after preprocessing, wherein the pit data set comprises a preprocessed pit image; S2, constructing an improved YOLOv3 pit detection network model; S3, inputting a training data set of the pit data set into the improved YOLOv3 pothole detection network model for training, and when the improved loss function approaches zero, obtaining a parameter optimal solution of the improved YOLOv3 pothole detection network model; and S4, inputting the pit data set into the improved YOLOv3 pit detection network model into which the parameter optimal solution is substituted to obtain a pit detection result. According to the invention, the problems that the real-time performance of pit detection needs to be guaranteed, and the accuracy needs to be further improved are solved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an improved YOLOv3-based pothole detection method. Background technique [0002] Potholes are bowl-shaped road obstacles with irregular closed curve openings, which can easily change the driving state of unmanned vehicles and eventually lead to traffic accidents. The traditional pothole detection algorithm mainly uses geometric features such as the texture of the pothole as the basis for pothole detection, which has the problems of low pothole detection accuracy and insufficient real-time performance. At present, deep learning has become the mainstream method of object detection, including the detection of potholes using two-stage, multi-stage and single-stage algorithms. The two-stage detection algorithm Faster RCNN and the multi-stage detection algorithm Cascade RCNN have high detection accuracy, but cannot meet the real-time requirements. On the contrary, the single...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06N3/045G06F18/23213
Inventor 罗春雷黄强胡均平罗睿袁确坚段吉安夏毅敏赵海鸣
Owner CENT SOUTH UNIV
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