Research on YOLO model optimization of multi-modal data processing attention mechanism based on deep learning

In this study, a YOLO model method combining LightGBM and multi-modal data processing attention mechanism optimization is proposed to enhance the effectiveness and accuracy of object detection. The traditional YOLO model has the problem of insufficient utilization of multi-modal data when processing image data, while LightGBM has significant advantages in processing structured data. Therefore, this paper designs and implements an optimization framework by embedding CBAM module into the extraction of features part of YOLOv5 model to improve the capability of feature representation. Concurrently, LightGBM is employed to handle structured data, and a fusion module is used to combine the features from both sources with appropriate weighting.