Single-Shot Detector with Multiple Inference Paths
Shoufa Chen, Xinggang Wang · 2019
In this paper, we investigate the problem of resource-constrained object detection using deep learning, which is a challenging problem in real-world applications. To address this problem, we propose a single-shot detector with multiple inference paths based on a multi-scale DenseNet. Experiments are carried out on the PASCAL VOC and COCO datasets, and results show that, with significant computation reduction, our detection network obtains comparable performance corresponding to the single-shot detectors, such as YOLO and SSD.