Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN

Abstract

This study presents a simple and effective Mask R-CNN algorithm for more rapid detection of vehicles and pedestrians. The method is of practical value for anticollision warning systems in intelligent driving. Deep neural networks with more layers have greater capacity but also have to perform more complicated calculations. To overcome this disadvantage, this study adopts a Resnet-86 network as a backbone that differs from the backbone structure of Resnet-101 in the Mask R-CNN algorithm within practical conditions. The results show that the Resnet-86 network can reduce the operation time and greatly improve accuracy. The detected vehicles and pedestrians are also screened out based on the Microsoft COCO dataset. The new dataset is formed by screening and supplementing COCO dataset, which makes the training of the algorithm more efficient. Perhaps, the most important part of our research is that we propose a new algorithm, Side Fusion FPN. The parameters in the algorithm have not increased, the amount of calculation has increased by less than 0.000001, and the mean average precision (mAP) has increased by 2.00 points. The results show that, compared with the algorithm of Mask R-CNN, our algorithm decreased the weight memory size by 9.43%, improved the training speed by 26.98%, improved the testing speed by 7.94%, decreased the value of loss by 0.26, and increased the value of mAP by 17.53 points.