一种用于智能零售视觉结算的增量学习方法OA北大核心CSTPCD

To deal with the incremental learning issue of intelligent retail automatic check-out,a novelty data-argument-based triplet model is proposed,which consists of the synthesizer network,the renderer network and the detector network.Specifically,the synthesizer network and the renderer network learn collaboratively to generate rendered check-out images with distribution close to the real data by synthesizing and rendering the single-product example images with data augmentation.In the incremental learning phase,the original and new product example images are collaborative learned to generate rendered check-out images containing new products.These rendered check-out images are utilized to train the product detector network.The model obtained by training in this way has the ability to recognize both of original products and new products.The experimental results show that the model has more excellent ability to overcome catastrophic forgetting compared to the existing incremental learning methods.The incremented check-out accuracy is 64.90% with a forgetting rate of 3.63% ,which is better than the state-of-the-art method of 4.38% .