Water turbine runner blade defect detection method based on YoloV4-Lite network

A defect detection and runner blade technology is applied in the field of defect detection of hydraulic turbine runner blades based on YoloV4-Lite network, which can solve the problems of large size of hydraulic turbine and difficulty in collecting fault data.

Active Publication Date: 2021-08-10
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0009] At present, due to the large size of the turbine and the difficulty in collecting fault data, no schol

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  • Water turbine runner blade defect detection method based on YoloV4-Lite network
  • Water turbine runner blade defect detection method based on YoloV4-Lite network
  • Water turbine runner blade defect detection method based on YoloV4-Lite network

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[0038] The embodiment of the present invention will be explained in detail below in conjunction with the accompanying drawings. The examples given are only for the purpose of illustration, and cannot be interpreted as limiting the present invention. The accompanying drawings are only for reference and description, and do not constitute the scope of patent protection of the present invention. limitations, since many changes may be made in the invention without departing from the spirit and scope of the invention.

[0039] In order to accurately detect the surface defects of the turbine runner blades, the embodiment of the present invention provides a method for detecting the defects of the turbine runner blades based on the YoloV4-Lite network, using the YoloV4-Lite algorithm in the field of deep learning to detect Surface defects (cavitation, cracks, etc.) are detected, and the algorithm is improved. Such as figure 1 As shown, the method mainly includes steps:

[0040] S1: B...

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Abstract

The invention relates to the technical field of water turbine runner blade defect detection, and particularly discloses a water turbine runner blade defect detection method based on a YoloV4-Lite network, and the method comprises the steps: S1, constructing a defect detection network based on the YoloV4-Lite network; S2, performing picture collection on different defect positions of the turbine runner blade to obtain thousand or more defect pictures; S3, preprocessing the defect picture acquired in the step S2 (processing by using LabelImg software according to a Pascal VOC 2012 format) to obtain a data set; and S4, training, testing and verifying the defect detection network by adopting the data set. According to the invention, the backbone extraction network CSPDarkNet53 network of YoloV4-Lite is replaced by the MobileNet network, and the MobileNet network is a real-time lightweight network, so that the network detection speed can be improved, and the network parameters can be greatly reduced. Experimental results show that the accuracy rate of the defect detection network can reach 97.48%, the network parameter quantity of the MobileNetV3 only needs 37.35 MB and is reduced by 206.94 MB compared with that of CSPDarkNet53, the FPS reaches 44.68, and the method has the advantages of being high in accuracy rate, low in memory storage and real-time.

Description

technical field [0001] The invention relates to the technical field of defect detection of hydraulic turbine runner blades, in particular to a method for detecting defects of hydraulic turbine runner blades based on the YoloV4-Lite network. Background technique [0002] At present, most of the methods used in the detection of turbine runner blades are manual detection, but manual detection has low accuracy, high cost and high work risk. During manual inspection, the water turbine needs to stop running, and each manual inspection takes about a week, which will bring huge economic losses. Therefore, it is particularly important to transform from traditional manual inspection to machine inspection. In the process of machine detection, choosing a more suitable algorithm will determine the accuracy of fault detection. [0003] In order to solve the problem of uneven distribution in the process of sample processing, there is currently a method to use the MC-SMOTE algorithm to di...

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Application Information

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IPC IPC(8): G06T7/00G06K9/46G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/10004G06T2207/30164G06V10/40G06N3/045
Inventor 刘成余波巫尚蔚周群李显勇
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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