Object Detection Algorithm Based on Improved Faster R-CNN

Object detection is a hot topic in computer vision research, among which faster region-based convolutional neural network (Faster R-CNN) has guiding significance for object detection. Aiming at the problem of the low accuracy of the Faster R-CNN algorithm in object detection, the data is enhanced first. Then, the extracted feature map is trimmed, and bilinear interpolation is used to replace the region of interest pooling operation. Soft-non-maximum suppression (Soft-NMS) algorithm is used for classification. Experimental results show that the accuracy of the algorithm is 76.40% and 81.20% in PASCAL VOC2007 and PASCAL VOC07+12 datasets, which is 6.50 percentage points and 8.00 percentage points higher than that of the Fast R-CNN algorithm, respectively. Without data enhancement, the accuracy on the COCO 2014 dataset is improved by 2.40 percentage points compared with that of the Faster R-CNN algorithm.