Electrical equipment defect image detection method based on detection reference point offset analysis
An image detection and power equipment technology, applied in the field of power equipment defect image detection based on detection reference point offset analysis, can solve the problems of few power equipment defect samples and low recognition rate, and achieve the effect of solving the problem of less training samples
Active Publication Date: 2021-08-27
ANHUI NANRUI JIYUAN POWER GRID TECH CO LTD
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[0005] The purpose of the present invention is to solve the defects of few defect samples and low recognition rate of electric equipment in the prior art, and provide a method for detecting electric equipment defect images based on detection reference point offset analysis to solve the above problems
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[0054] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:
[0055] Such as figure 1 As shown in the present invention, a defect image detection method for electric equipment based on detection reference point offset analysis includes the following steps:
[0056] The first step is the acquisition of the training image set: acquire and preprocess the known defect images of electrical equipment, scale the picture I to a size of 512*512, and summarize it as a training image set.
[0057] The second step is the construction of the defect image detection network: the defect image detection network is constructed through the basic feature representation network, deconvolution network and local detection network. The defect image detection network designed by the present invention belongs to the on...
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The invention relates to an electrical equipment defect image detection method based on detection reference point offset analysis. Compared with the prior art, the defects that the number of defect samples of electrical equipment is small and the recognition rate is low are overcome. The method comprises the following steps: obtaining a training image set; constructing a defect image detection network; training a defect image detection network; acquiring a defect image of to-be-detected electrical equipment; and detecting a defect image of the electrical equipment. Through the design of the central point prediction network, the target width and height prediction network and the central point offset network, the defect detection range of the electrical equipment can be enlarged and is not limited to the central point area of the image; meanwhile, according to the weight value of the peripheral detection area, the proportion of all loss values of the peripheral detection area in training learning is automatically determined; and the problem of few training samples of the power design defect image is further solved.
Description
technical field [0001] The invention relates to the technical field of electrical equipment, in particular to an electrical equipment defect image detection method based on detection reference point offset analysis. Background technique [0002] Current power equipment defect detection methods mainly include traditional methods and deep learning methods, and deep learning methods are basically divided into two-stage detection methods and single-stage methods according to the detection network architecture. The two-stage methods are mainly composed of convolutional neural networks and region proposal networks. , it is generally necessary to set a calibration frame with a certain area and aspect ratio in advance, and return a large number of possible area frames according to these calibration frames, and then further judge which type of these large number of possible area frames are, and the single-stage detection method is in the extraction volume After the features are integ...
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IPC IPC(8): G06T7/00G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06T7/0002G06N3/084G06T2207/10004G06T2207/20081G06T2207/20084G06V10/25G06N3/045G06F18/214
Inventor 杨建旭刘群童旸华雄程晗王成进吴旻鲍现松徐贺杨帆
Owner ANHUI NANRUI JIYUAN POWER GRID TECH CO LTD