Design And Implementation Of 3D Object Detection Method Based On Multimodal Fusion

3D object detection is the key technology for 3D environment perception and reconstruction,and the cornerstone of the interaction between machines and the world.It has broad application prospects in scenarios such as autonomous driving,autonomous navigation,and intelligent robots.Compared with 2D object detection,3D object detection not only needs to identify the type of target object,but also needs to identify the precise position of the object.From the perspective of information theory,the single-modal data of point cloud and image is not suitable for the task of 3D object detection.Therefore,it is necessary to integrate multiple modal data for complementary enhancement of feature information.This paper aims to conduct research on modal data representation and alignment,fusion and enhancement of associated features,and balance multi-task objective function weights in the research of multi-modal 3D object detection,and to design a 3D object detection algorithm with high precision and high robustness.First of all,for the problem of modal data representation and alignment,fusion and strengthening of associated features,this paper realizes the feature representation of image and point cloud modal data based on Faster-RCNN and PointNet2,uses the dimension reduction layer to achieve feature alignment,and uses bilinear pooling The fusion method fuses features and proposes a 3D object detection method based on bilinear pooling fusion.The fusion algorithm of this method minimizes the difference in feature distribution through the modal feature difference measurement function,and co-evolves the associated features of different modalities to achieve the purpose of complementary enhancement of modal features.The experimental comparison proves that the model is complementary to the point cloud and image features from the perspective of associated features on the SUN RGB-D dataset,and the detection accuracy of each category has been improved.Secondly,in order to balance the weight of multi-task objective functions,this paper designs a synchronization control method based on model learning state FOG metrics on the dynamic adjustment of modal model weight algorithm.The FOG index reflects the fitting ability,overfitting ability,and generalization ability of the model.This method dynamically updates the weight value of each sub-objective function by measuring the FOG value of the current model.The method also introduces limited solution constraints,narrows the solution space,realizes a small increase in calculation,and achieves a better multi-task learning weight solution.On the SUN RGB-D dataset,the experimental comparison proves that the detection effect of this method is improved to a certain extent compared with the fixed weight method.Finally,this paper designs and implements a multi-modal model management system,which can realize the full life cycle management of model design,training,deployment and monitoring.On this system,the design of the multi-modal model can be completed through the graphical interface,and the deployment,monitoring and management of the model can be realized through the form of tasks.The system provides a convenient tool for scientific research in related fields.This paper first expounds the research background and significance of multi-modal 3D object detection,and introduces the research status and related technologies in this research field.Then,starting from the requirements of the application scenario,the algorithm research and experiment are carried out,and the detailed design and implementation of the system are completed.Finally,the work content of this paper is summarized,and the future extended research is prospected.