Multimodal information fusion dynamic target recognition for autonomous driving
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Abstract:
A multi-modal information fusion based object recognition method for autonomous driving is proposed to address the vehicle and pedestrian detection challenge in autonomous driving environments. The method first improves ResNet50 network based on spatial attention mechanism and hybrid null convolution. The standard convolution is replaced by selective kernel convolution, which allows the network to dynamically adjust the size of the perceptual field according to the feature size. Then, the sawtooth hybrid null convolution is used to enable the network to capture multi-scale contextual information and improve the network feature extraction capability. The localization loss function in YOLOv3 is replaced with the GIoU loss function, which has better operability in practical applications. Finally, human-vehicle target classification and recognition algorithm based on two kinds of data fusion is proposed, which can improve the accuracy of the target detection. Experimental results show that compared with OFTNet, VoxelNet and FASTERRCNN, the mAP index can be improved by 0.05 during daytime and 0.09 in the evening, and the convergence effect is good.
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Supported by National Natural Science Foundation of China（51975217）.
