A Collaborative Neural Network-Based Automatic Object Recognition Method for Environmental Space Under Virtual Reality Scenes

The widespread application of virtual reality has made automatic target recognition in environmental space particularly important. However, traditional image recognition methods faced challenges due to the complex and diverse environmental characteristics in virtual reality scenes. It is easily influenced by occlusion, noise, and other factors. To deal with the issue, this paper utilizes the combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and proposes a collaborative neural network-based automatic object recognition method for environmental space under virtual reality scenes. Specifically, CNN is mainly responsible for feature extraction of environmental scene images, while RNN is used to model the spatial relationships of image sequences. Then, a data augmentation method is introduced in virtual reality scenarios, which can generate more training samples on a limited dataset, thereby improving the model’s generalization ability. Meanwhile, in order to address issues such as occlusion and noise, a multi-scale feature fusion strategy is further developed to improve the robustness of target recognition by multiscale feature fusion. Through experimental verification using actual data collected in virtual reality scenes, the results show that the proposed method has achieved excellent performance and strong adaptability compared with traditional methods.