Abstract: With the development of intelligent driving,the precision requirements for target detection are getting higher and higher,especially for small targets that are far away.In the neck of two-stage object detection network,although the feature fusion of semantic information and location information is more effective for large targets if the bottom-up fusion method is used,it will cause big information loss to small targets.To address this problem,we propose neighbor feature pyramid networks(NFPN) method of feature fusion of neighbor layers,the Double RoI(Region of Interest) method to fuse the FPN and NFPN features,and the recursive feature pyramicl(RFP) method.Using Faster RCNN 50 as the benchmark,the mean average precision(mAP) of our model in the Lisa data set has increased by 2.6% while using NFPN,Double RoI and RFP.On the VOC2007 data set,using the VOC07+12 train data set for training,VOC2007 test as the test set,and Faster RCNN101 as the baseline,the mAP of our model both used NFPN,Double RoIE and RFP has increased by 6%,and the object detect accuracy of large,medium and small targets is improved at the same time.

Key words: Autonomous driving, Computer vision, Deep learning, Feature fusion, Object detection