Multisensor Decision-Level Fusion Network Based on Attention Mechanism for Object Detection

To solve the problem of low accuracy caused by threshold constraints in traditional decision-level fusion methods, this article proposes a deep learning method based on an attention mechanism to fuse the 3-D information of sensors. The proposed model based on attention mechanism (AFnet) can improve the accuracy of the detection system without relying on traditional constraints. The AFnet model decouples the correlation between the data by the encoder and fully utilizes the nonlinear fitting capability of deep learning. The adaptive fusion can be realized under data scale and result bias, which effectively solves the problem caused by traditional methods in the case of vehicle occlusion and overlap. The depth information and object detection networks are combined by embedding, which ensures that cameras can achieve spatial detection of vehicles and overcome the limitations of the 2-D plane. The redefined clustering method takes into account the spatial position and velocity attribute, which can effectively distinguish high-density overlapping point clouds. Finally, experimental results in NuScenes and Carla show that the proposed fusion method does not rely on traditional rule constraints, and improves the accuracy of object detection. The fusion model of AFnet presents a state-of-the-art on fusion matching accuracy of 99.11%.