# DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention Source: [DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:50.644Z Authors: Huimin Shi, Quan Zhou, Yinghao Ni, Xiaofu Wu, Longin Jan Latecki Published: 2021-10-31T13:38:16.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()