Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices. To address the trade-off between computational cost and detection accuracy, this paper presents a dual path network, named DPNet, for efficient object detection with lightweight self-attention. In backbone, a single input/output lightweight self-attention module (LSAM) is designed to encode global interactions between different positions. LSAM is also extended into a multiple-inputs version in feature pyramid network (FPN), which is employed to capture cross-resolution dependencies in two paths. Extensive experiments on the COCO dataset demonstrate that our method achieves state-of-the-art detection results. More specifically, DPNet obtains 29.0% AP on COCO test-dev, with only 1.14 GFLOPs and 2.27M model size for a 320x320 image.
@article{arxiv.2111.00500,
title = {DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention},
author = {Huimin Shi and Quan Zhou and Yinghao Ni and Xiaofu Wu and Longin Jan Latecki},
journal= {arXiv preprint arXiv:2111.00500},
year = {2021}
}