Laser & Optoelectronics Progress, Volume. 57, Issue 10, 101009(2020)
Object Detection Algorithm Based on Improved Faster R-CNN
ing 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.
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Bing Zhou, Runxin Li, Zhenhong Shang, Xiaowu Li. Object Detection Algorithm Based on Improved Faster R-CNN[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101009
Category: Image Processing
Received: Aug. 28, 2019
Accepted: Oct. 18, 2019
Published Online: May. 8, 2020
The Author Email: Li Runxin (rxli@kmust.edu.cn)