Research on pedestrian detection using optimized mask R-CNN algorithm in low-light road environment

Abstract

Aiming to the performance degradation of the object detection algorithms in low-light environment, the image fusion module (MSRCR-IF) proposed in this paper is introduced into the object detection network based on the object detection algorithm of Mask R-CNN. This proposed fusion algorithm adjusts Region Proposal Network (RPN) and delete instance mask branch to achieve better pedestrian detection performance of algorithm in low-light environment. The experimental results reveal that the algorithm proposed in this paper has better detection performance than other current mainstream algorithms in COCO2017 data set, and the average detection accuracy of 85.05% was achieved under the self-built low-light road environment data set, which was 4.66% higher than before improvement. In order to verify the effectiveness of the improved algorithm, a real car data test was conducted, and the test results showed that the improved method can effectively improve the detection effect of pedestrians in low light conditions.