Image Recognition Processing Technology Based on Virtual Reality Technology and Adaptive Feature Fusion

Image recognition technology has seen gradual enhancement due to advancements in deep learning algorithms. Nonetheless, target detection algorithms encounter challenges like low positioning accuracy and missed and false detections in multiple-target, large-scale, and complex environments. Therefore, in order to improve the image recognition processing effect, an adaptive weighted fusion-YOLOv4 object detection algorithm network structure is designed and applied to object detection in virtual reality scenarios. Including the backbone network, neck network, and prediction network. An adaptive feature fusion module is incorporated into the backbone network to enhance the learning capacity of residual blocks. Additionally, a broad-scale cross-stage fusion network is implemented in the neck network to minimize information loss during feature fusion and facilitate more effective utilization of feature information by the neck network. At the same time, the SIoU loss function is used to improve the convergence speed. When applied to natural scenery, urban roads, office environments, and plateau deserts, the Adaptive Weighted Fusion YOLOv4 algorithm achieved mAP scores of 0.9056, 0.9143, 0.9106, and 0.9812, respectively. Compared to other methods, it exhibited the least fluctuation amplitude in the image signal, with maximum amplitude reduction rates at 14.25%, 17.36%, and 22.36%, respectively. This method enhances network fusion and feature extraction, resulting in superior detection accuracy during image recognition processes.