System and method for detecting product shape integrity based on asynchronous time domain vision sensor

Abstract

The invention discloses a system and a method for detecting the product shape integrity based on an asynchronous time domain vision sensor. The method comprises an algorithm for checking the integrity of an overall structure of the system and the product shape. The overall structure of the system comprises a camera adopting a time domain vision sensor and a processing portion containing a caching and calculating unit. A software algorithm takes activity events outputted by a TVS (temporal vision sensor) as input, and AE (activity event) sets with the address and the time being discrete represent actual products moving on an assembly line. Shape parameters of each moving object are calculated according to address distribution conditions of each AE set, and checking for the product shape integrity is realized through comparing the parameters reflecting the product shape with shape parameters of an integral product. Compared with an existing system, the system disclosed by the invention has significant performance and cost advantages and has wide application prospects.

Description

Technical field [0001] The invention relates to multiple technical fields such as machine vision, digital image processing and circuit design. More specifically, it is a product shape integrity detection system and inspection method based on asynchronous time domain vision sensors. Background technique [0002] Shape integrity inspection is one of the important contents of industrial product quality inspection. For simple industrial assembly line products, such as tablets, cigarettes, small mechanical parts, etc., by using a computer to process and analyze their images, the shape integrity can be automatically and accurately judged, which greatly improves work efficiency and reduces production costs. The image sensor in and in the industrial camera is the core component of the entire machine vision system. The current image sensors used in machine vision systems are all based on the "frame sampling" method, and the imaging principle is: [0003] 1. After all pixels are reset at ...

AI Technical Summary

Problems solved by technology

With the improvement of image resolution, these redundant information bring huge processing and storage pressure to the system;

[0009] (2) Large delay

This large delay between "change and perception" brings great difficulties to the detection and recognition of high-speed moving targets. The lower the shooting frame rate and the faster the movement speed, the more obvious the discontinuity of detection

If a higher frame rate shooting speed is adopted, the processing and storage pressure brought by (1) will be even greater

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