Human Detection Based on Improved Mask R-CNN

Abstract

Human detection has been widely concerned by academic and industrial circles. The gradual maturity of deep learning framework further improves the accuracy and speed of detection. However, the relatively mature human detection methods cannot get accurate detection results because of the complex background, shooting angle and many limitations of human behaviour. Aiming at solving the problems of human detection in complex scenes, a novel human detection algorithm based on the improved mask R-CNN framework is proposed by implementing the leading research results of object detection through deep learning. The algorithm combines ResNet and FPN to extract the features of the image, and then takes advantage of RoIAlign and fine-grained Slic to proofread the pixels. In the experiments that compared with the original mask R-CNN algorithm on the same data set was carried out to verify the effectiveness of the proposed algorithm. The mAP and AR value of the improved mask R-CNN algorithm is greater than that of the mask R-CNN algorithm when the IoU is 0.5-0.95, showing that the improved mask R-CNN framework was able to detect human from video better.