Abstract:  A real-time deep neural network for object detection was proposed based on a real object detection model of YOLO and an algorithm of Faster R-CNN to improve the running speed of deep learning neural network and to meet the real-time requirements of the algorithm for intelligent-driving. The neural network retained the secondary detection mode of the R-CNN series and region proposal network (RPN), removed the priori box, and used the YOLO to predict the location directly. The position prediction error caused by ROI-pooling in the Faster R-CNN was decreased, combined with a ROI-Align method in a Mask R-CNN. The improved network was tested on KITTI dataset. The results show that the improved neural network detection takes only 38 ms at once detection, the detection average accuracy of improved networks is higher than YOLO and Faster RCNN with a good generalization ability for objects with different sizes at a faster speed with a higher detection precision.

Key words: intelligent-driving cars ,  , environmental perception ,  object detection , deep learning ,  visual perception , convolutional neural network（CNN）