LAUSR

Optical performance evaluation is a critical process in the production of collimating lenses. However, the current visual inspection of lens light-spot images is inefficient and prone to fatigue. Intelligent detection based on machine vision and deep learning can improve evaluation efficiency and accuracy. In this study, a dual-branch structure light-spot evaluation model based on deep learning is proposed for collimating lens optical performance evaluation, and a lens light-spot image dataset is built, containing 9000 images with corresponding labels. Experimental results show that the proposed model achieves accurate classification of lens optical performance evaluation. Combined with the proposed weighted multi-model voting strategy, the performance of the model is further improved, and the classification accuracy successfully reaches 98.89%. Through the developed application software, the proposed model can be well applied to the quality inspection in collimating lens production.