Multi-Sensor Fusion Module for Perceptual Target Recognition for Intelligent Machine Learning Visual Feature Extraction

This paper investigates the multi-sensor fusion perceptual target recognition based on machine learning visual feature extraction. On the basis of the efficient neural network model (Enet), an improved efficient semantic segmentation network model (Enet-CRF) is designed according to the requirements of the research content. This network model merges a CRF-RNN back-end optimization network to the original Enet, and further improves the classification performance of the network by adding constraints on the position relationship between image pixels and RGB information. The experiments indicate that the designed Enet-CRF has a certain improvement in obstacle classification performance, especially for small-scale obstacles such as pedestrians and bicycles. By deep fusion of LiDAR and vision sensors at the data level and higher-order features, this method adds high-precision radar information to the original Enet-CRF network model, thereby further enhancing the accuracy of obstacle classification and achieving an obstacle recognition method. This approach can not only accurately classify obstacles, but also attain accurate spatial information of obstacles, with good real-time performance.