Improved YOLO-V3 metal processing surface defect detection method

A metal processing and defect detection technology, applied in image analysis, image enhancement, instruments, etc., can solve the problems of poor detection effect, inability to accurately identify small objects, low recall rate, etc., and achieve accurate detection and reduce the downsampling factor Effect

Active Publication Date: 2020-06-16
CHONGQING UNIV
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Problems solved by technology

[0004] The above method can detect conventional defects on the appearance of industrial devices through the traditional neural network, but when detecting processing defects on the metal surface, since the defects such as crushing and bruising on the metal surface are usually small in size, the defects in the input image The pixel area is also very small. If the traditional neural network is used, because its feature extraction backbone network usually has a large receptive field, although it is convenient for image classification, it will compromise the spatia...

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  • Improved YOLO-V3 metal processing surface defect detection method
  • Improved YOLO-V3 metal processing surface defect detection method
  • Improved YOLO-V3 metal processing surface defect detection method

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[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0046] Such as figure 1 As shown, it is a flow chart of a specific implementation of an improved YOLO-V3 metal processing surface defect detection method disclosed in the present invention. In this embodiment, the improved YOLO-V3 includes a feature extraction backbone network . A multi-scale fusion module and a multi-classifier module, wherein the metal processing surface defect detection method includes:

[0047] S1. Acquiring metal surface processing images;

[0048]S2. Input the metal surface processing image into the feature extraction backbone network to extract feature maps of different scales. The feature extraction backbone network has no pooling layer and the downsampling factor is smaller than that of Darknet-53, and the number of convolutional layers is less than that of Darknet. -53 convolutional layers;

[0049] Since the pixel area of ​​the...

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Abstract

The invention discloses an improved YOLO-V3 metal processing surface defect detection method. The method comprises the steps of acquiring a metal surface processing image; extracting feature maps of different scales from the feature extraction backbone network, wherein the feature extraction backbone network has no pooling layer, the downsampling factor is smaller than the downsampling factor of Darknet-53, and the convolution layer number is smaller than the convolution layer number of Darknet-53; inputting the feature map into a multi-scale fusion module for local feature fusion to obtain afused feature map; and inputting the fusion feature map into a multi-classifier module for metal processing surface defect positioning and classification. According to the invention, a pooling layer is omitted and a downsampling factor is reduced, and the problem of low-level feature loss generally caused in the down-sampling process of pooling operation is avoided. According to the method, the structural characteristic of high resolution of the deep features is guaranteed; the high resolution characteristic of the low-level features and the high semantic information characteristic of the deepfeatures are combined through local feature fusion; and finally accurate detection of small target defects on the metal machining surface is achieved.

Description

technical field [0001] The invention relates to the field of defect detection, in particular to an improved YOLO-V3 metal processing surface defect detection method. Background technique [0002] The detection of surface defects in metal processing is an important process in the manufacture of metal parts. Common defects on metal surfaces include scratches, crushes, and bruises. In precision mechanical equipment, the above defects may lead to potential problems such as deterioration of working conditions, affect the transmission accuracy of equipment, generate noise, and even cause equipment damage, resulting in huge losses. Affected by tooling iron residues, improper workpiece clamping, physical collisions, etc., surface defects in metal processing often occur, and batch problems are prone to occur. At present, manufacturing enterprises mainly use manual sampling inspection for the detection of metal processing surface defects, which has problems such as relying on manual...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0004G06T2207/20081G06T2207/20084G06T2207/30136G06T2207/30164G06F18/24G06F18/253Y02P90/30
Inventor 苏迎涛鄢萍易润忠胡靖华
Owner CHONGQING UNIV
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