Research on Object Detection in Campus Scene Based on Faster R-CNN

Abstract

This paper focuses on the research of object detection technology in Smart Campus scene. Firstly, an object detection algorithm is constructed based on Faster R-CNN two-stage detector, which is trained on COCO2017 and VOC0712 public datasets, and the performance of the trained detector is evaluated. On this basis, the actual image data is collected from the campus scene, and divided into different groups according to the shooting angle, lighting conditions, occlusion, etc. After using the algorithm to detect, the results are analyzed systematically. In general, occlusion between objects is more likely to lead to unsatisfactory detection results. In the future, we still need to find a non-maximum suppression method which can effectively distinguish the same object repeated detection and occlusion between objects.