System and method for visual detection of redundant objects based on deep learning in mechanical assembly

Abstract

The invention discloses a system and method for visual detection of redundant objects based on deep learning in mechanical assembly. The detection system includes a workbench, an adjustable angle camera, a light source, a vision controller, etc. The vision controller processes and analyzes the images obtained from the camera to identify the objects in it, and then distinguish the assembly parts and redundant objects; the detection methods include The steps are as follows: obtain the multi-angle assembly area image, preprocess the image and input it into the trained target detection network model for feature extraction to predict the position and type of the object, then judge whether each object is redundant, and mark the redundant object position and call the police. The invention can assist manual detection of redundant objects in a specific area from multiple angles in real-time during the assembly process, has the advantages of high detection accuracy, strong real-time performance, flexible use, etc., can reduce the introduction of redundant objects in the assembly process, and enhance product reliability sex.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a system and method for visual detection of redundant objects based on deep learning in mechanical assembly. Background technique [0002] Residues refer to all substances in the product that are entered from the outside or generated inside and have nothing to do with the specified state of the product. In the assembly process of large-scale equipment with high reliability and high safety, due to the complex structure of equipment and various production processes and assembly procedures, it is very easy to introduce redundant materials. For example, improper operation of workers may bring in objects such as screws, washers, hair strands, and rag residues; processes such as welding and machining may introduce redundant objects such as welding slag and metal chips. If these redundant substances are left in the equipment, it will leave serious safety hazards, whic...

AI Technical Summary

Problems solved by technology

If these redundant substances are left in the equipment, it will leave serious safety hazards, which may affect the normal operation of high-reliability equipment, and even cause malfunctions and safety accidents.

[0003] After long-term development, the current redundant detection and control methods mainly include visual and auditory detection, endoscopic detection, X-ray perspective detection, ultrasonic detection, Matera detection and particle collision noise detection, etc. , with more and stricter detection steps

However, most automatic detection methods are only suitable for fully assembled objects, and manual inspection is often used in assembly, and there are still problems such as large human factors and easy missed inspections.

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