Computer vision models application in the current system on object detection tasks

Abstract

The implementation of object detection algorithms would be helpful to the various fields of the current time. When object detection is applied to the surveillance camera system, it will be more efficient to locate crimes or find lost kids. This paper will investigate the performance of different object detection algorithms in a real-world scenario. With experimentation, CenterNet++ outperforms YOLO and MaskRCNN, two traditional and classic object detection algorithms, on the MS COCO dataset, which concludes that CenterNet++ can ensure both accuracy and speed.