Vehicle Detection for Unmanned Systems Based on Multimodal Feature Fusion

Vehicle Detection for Unmanned Systems Based on Multimodal Feature Fusion

Blog Article

This paper proposes a 3D vehicle-detection algorithm based on multimodal feature fusion to address the problem of low vehicle-detection accuracy in unmanned system environment awareness.The algorithm matches the coordinate relationships between the two sensors and reduces sampling errors by combining the millimeter-wave radar and camera calibration.Statistical filtering is used to remove redundant points from the millimeter-wave radar data to reduce outlier interference; a multimodal feature fusion module is constructed to fuse the point cloud and image information using pixel-by-pixel averaging.

Moreover, Infusion and Injection feature pyramids are added to extract fused high-level feature information, which is used to improve detection accuracy in complex road scenarios.A feature fusion region proposal structure was established to generate region proposals based on the high-level feature information.The vehicle detection results were obtained by matching the detection frames in their vertices after removal of the redundant detection frames using non-maximum suppression.

Experimental results from the SPORT PROTEIN VANILLA KITTI dataset show that the proposed method improved the efficiency and accuracy of vehicle detection with the corresponding average of 0.14 s and 84.71%.