Improved Object Detection Algorithm Based on Faster RCNN

This paper studies the target detection algorithm based on Faster R-CNN. Aiming at the insufficient regression accuracy of the target prediction box, an improved target detection algorithm based on Faster R-CNN is proposed. Firstly, ResNet 50 residual network is selected and the feature pyramid (FPN)is introduced to improve the ability of target detection. Secondly, GIOU is introduced to optimize the anchor frame to improve the positioning problem of the target candidate frame. Finally, a bilinear interpolated ROI Alian is used to replace the original ROI pooling, which avoids the pixel error caused by two quantization operations. The data set Pascal VOC 2012 is used for training and testing, and it is verified that the proposed algorithm improves the mAP by 5.4% compared with the original algorithm.