Research on Industrial Small Sample Object Data Enhancement Strategy for Robot Vision

For the problems of insufficient number of samples and lack of sample diversity faced by industrial scenes in robot vision applications, this paper proposes an improved data enhancement strategy for industrial small-sample objects. The method combines a stochastic algorithm with a deep learning-based image generation technique to generate a large number of realistic real-life style target images from a small number of template images, thus effectively improving the recognition ability of target objects in complex backgrounds. In addition, virtual reality technology is introduced to generate virtual artifact images with similar styles to those captured by actual cameras using a virtual engine, further enriching the diversity and coverage of the dataset. This technique also employs an advanced algorithm that incorporates a convolutional network and a self-attention module in a cyclic adversarial generative network (CycleGan) to achieve efficient generation and style migration of industrial object images. This data enhancement strategy enriches the training data of the robot vision system and significantly improves the robot's stable detection ability and data quality of targets in complex environments.