A MULTI-TARGET DETECTION ALGORITHM OF ROAD FOR UNMANNED DRIVING SCENE
Aimed at the problem of high false detection rates of object detection in unmanned driving scene,a multi-target detection algorithm with improved YOLOv3 is proposed.The groups convolution kernel was introduced into the original feature network Darknet53 to replace the original convolution kernel,which reduced the complexity of convolution operation.The original feature fusion was improved to make the fusion of different scales more fully,and it improved the detection effect of occluded and small targets.The CIoU loss function was constructed to make the network convergence better.Experimental results show that the average accuracy of the improved YOLOv3 algorithm is increased by 1.71％,and the false detection rate is reduced by 12％,which is significantly better than the YOLOv3 algorithm.
DriverlessMulti-target detectionGroup convolutionYOLOv3CIoU loss function
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