Road Environment Object Detection Based on Improved SFA3D
The traditional traffic signal lamp with fixed duration cannot adapt to the society development gradually.The intelligent traffic signal lamp control system technology which controls the traffic light duration according to the real-time traffic flow and human flow at the intersection is gradually accepted by the society.Aiming at the problem that the detection accuracy of road environment targets is not high,the attention mechanism and the optimized model loss function are used to improve the SFA3D object detection framework,and the objects of traffic participation elements in the scene are accurately detected,and assist the traffic signalers to make decisions to obtain the best timing scheme.Firstly,the point cloud is converted into the input of bird's-eye view,and the feature pyramid is used to extract the features with the help of ResNet.Then,the feature information of the attention region is optimized and extracted by the attention mechanism.Finally,the object detection information is obtained by the linear weighted sum of each scale feature.Experimental results show that compared with the original SFA3D network,the algorithm can guarantee the real-time performance and improve the accuracy,which proves the effectiveness of the algorithm.
NETL
NSTL
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