The invention discloses a double-layer composite
quartz crucible surface defect detection method based on
machine vision, and relates to the technical field of
crucible surface defect detection, and the method comprises the following steps: a
conveyor belt conveys double-layer composite
quartz crucibles to a detection area at a preset speed to ensure that each
crucible can move in sequence on a
production line; and a detection visual field of a
machine vision
system is stably entered. According to the method, key features such as the
microstructure change index and the crack interval variability index are extracted, so that a
machine vision
system can identify complex defects more accurately, and misjudgment and missing detection are avoided. According to the method, the detection precision of complex defects is improved by enhancing
image processing. And in combination with a
machine learning model, the
system intelligently evaluates the defect complexity and dynamically adjusts the
conveyor belt speed. For simple defects, the high speed is kept, so that the production
rhythm is not influenced; for complex defects, the speed of the
conveyor belt is reduced, and time is provided for detailed analysis, so that the defect recognition rate is improved, and the product quality is ensured while efficient production is ensured.