Deep learning-based redundant object visual detection system and method in mechanical assembly

Abstract

The invention discloses a deep learning-based redundant object visual detection system and method in mechanical assembly. The detection system involves a worktable, an angle-adjustable camera, a lightsource, a visual controller and the like, wherein by processing and analyzing images acquired from the camera, the visual controller is used for identifying objects in the images and then distinguishing assembly parts and redundant objects. The detection method comprises the following steps of acquiring the images in a multi-angle assembly area, preprocessing the images, inputting the images intoa trained target detection network model for feature extraction to predict the positions and the types of the objects, then judging whether the objects belong to redundant objects, and marking the positions of the redundant objects and giving an alarm. The system and method can be used for assisting people with real time and multi-angle detection on the redundant objects in the specific area in the assembling process; the system and the method have the advantages of being high in detection accuracy, high in real-time performance, flexible in use and the like; and the introduction of the redundant objects in the assembling process can be reduced, and the product reliability is enhanced.

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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