DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention

• URL: http://arxiv.org/abs/2111.00500v1
• Date: Sun, 31 Oct 2021 13:38:16 GMT
• Title: DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention
• Authors: Huimin Shi, Quan Zhou, Yinghao Ni, Xiaofu Wu and Longin Jan Latecki
• Abstract summary: DPNet is a dual path network for efficient object detection with lightweight self-attention. It achieves 29.0% AP on COCO dataset, with only 1.14 GFLOPs and 2.27M model size for a 320x320 image.
• Score: 16.13989397708127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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