Visual Rotation Angle Measurement Method of Mechanism Model and Data Hybrid Driven Based on Deep Learning
To overcome the limitations of vision-based angle measurement methods,which are susceptible to system disturbances,this paper proposed a novel vision-based angle measurement approach,integrating a deep learning mechanism and data-driven model.This study validated the use of an isosceles triangle pattern on the axis for its effectiveness and rationality,establishing a mathematical model for calculating the angle based on the triangle pattern.This paper introduced a deep learning model based on YOLOv8.A hybrid angle measurement model was constructed by using linear combination..Experimental results demonstrate significant improvements in measurement accuracy with this hybrid model.Compared to using only principle-based model,the av-erage error is reduced by 1.125°,and the root mean square error decreases by 10.05°,maintaining high performance across vari-ous environmental test sets.This model effectively leverages the deep learning model's ability to adapt to random image disturb-ances,while retaining the constraints and stability of traditional mathematical models.The precision of visual angle measurements is improved and the adaptability to environmental changes and system disturbances is boosted.
rotation angle measurementmachine visiondeep learninghybrid model
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