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Automobile seat defect detection method based on multi-feature fusion machine learning

A multi-feature fusion and car seat technology, applied in neural learning methods, optical testing flaws/defects, instruments, etc., can solve the problems of single type, low timeliness requirements, and low efficiency of multi-template matching of detection images, and achieve Matching effects with high efficiency and high timeliness

Active Publication Date: 2021-01-15
NANJING ESTUN ROBOTICS CO LTD
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

There are many kinds of shapes, colors, and materials of car seats. If a template is created for each material, the efficiency of multi-template matching for new detection images is too low. The above method is suitable for scenarios with single or few types and low timeliness requirements.

Method used

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  • Automobile seat defect detection method based on multi-feature fusion machine learning
  • Automobile seat defect detection method based on multi-feature fusion machine learning
  • Automobile seat defect detection method based on multi-feature fusion machine learning

Examples

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specific Embodiment

[0096] Provide a kind of concrete embodiment below:Taking the detection and sorting of several different seat defects on a certain production line as an example, the specific implementation method of this scheme is illustrated:

[0097] This embodiment takes car seats as the object, including front row and rear row seats, such as Figure 5 As shown in the figure, the seat is one of the core components of the vehicle. It consists of a headrest 1, a backrest 2, a seat cushion 3, and a hand pillow 4. Identify the colors and materials of different parts of the seat, including black velvet, black leather, and gray leather. Whether there is a material category exception, such as Image 6 As shown, and detect whether there are damages and stains in each part, and screen out unqualified products. According to the classification identification and defect detection results, the industrial robot grabs the workpiece and automatically sorts the four categories of abnormal, damaged, stain...

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Abstract

The invention relates to the field of machine vision detection, in particular to an automobile seat defect detection method based on multi-feature fusion machine learning. The invention discloses an automobile seat defect detection method based on multi-feature fusion machine learning, and the method is suitable for materials with different colors and materials, does not need multi-template matching, and enables an industrial robot to automatically sort the materials to a specified region according to a detection result. A multi-feature fusion classifier is trained to identify material category information by extracting color and texture features of multiple categories of automobile seat materials, and material category abnormities are screened; according to the classification result, defect detection is carried out in combination with blob analysis, and whether damage and stains exist or not is judged; and the industrial robot receives the defect detection result, grabs the workpieces, and automatically sorts the workpieces to specified areas of abnormal material types, damage, stains and qualified products. According to the invention, multi-template matching is not needed, timeliness is high, and efficient sorting of multiple types of automobile seat material defects is achieved.

Description

technical field [0001] The invention relates to the field of machine vision detection, in particular to a method for detecting defects of automobile seats based on multi-feature fusion machine learning. Background technique [0002] Industrial robots, as multi-joint manipulators or multi-degree-of-freedom machine devices for the industrial field, have been widely used in various industries today, aiming to realize the automatic control of various applications on industrial production lines, such as palletizing, handling, sorting, etc., thereby Save time and economic cost. With the development of artificial intelligence and industry 4.0, machine vision, as an auxiliary tool to replace the human eye, processes, analyzes and calculates images, and cooperates with industrial robots. It is widely used in appearance inspection, identification and positioning in the industrial field to realize loading and unloading, Sorting and other functions. [0003] Different industries, simi...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46G06N3/04G06N3/08G06T7/00G06T7/11G06T7/136G06T7/187G06T7/194G06T5/00G01N21/88
CPCG06N3/084G06T7/0004G06T7/11G06T7/136G06T7/187G06T7/194G01N21/8851G06T2207/10024G06T2207/20081G06T2207/20084G06T2207/20024G06T2207/30108G01N2021/8887G06V10/56G06V10/50G06N3/045G06F18/2415G06F18/214G06F18/253G06T5/70
Inventor 史婷粟华张冶王杰高
Owner NANJING ESTUN ROBOTICS CO LTD
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