DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention

• URL: http://arxiv.org/abs/2209.13933v1
• Date: Wed, 28 Sep 2022 09:11:01 GMT
• Title: DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention
• Authors: Quan Zhou, Huimin Shi, Weikang Xiang, Bin Kang, Xiaofu Wu and Longin Jan Latecki
• Abstract summary: This paper presents a dual-path network, named DPNet, with a lightweight attention scheme for real-time object detection. DPNet achieves state-of-the-art trade-off between detection accuracy and implementation efficiency.
• Score: 15.360769793764526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection. To accelerate detection speed, lightweight detectors always have few convolution layers using single-path backbone. Single-path architecture, however, involves continuous pooling and downsampling operations, always resulting in coarse and inaccurate feature maps that are disadvantageous to locate objects. On the other hand, due to limited network capacity, recent lightweight networks are often weak in representing large scale visual data. To address these problems, this paper presents a dual-path network, named DPNet, with a lightweight attention scheme for real-time object detection. The dual-path architecture enables us to parallelly extract high-level semantic features and low-level object details. Although DPNet has nearly duplicated shape with respect to single-path detectors, the computational costs and model size are not significantly increased. To enhance representation capability, a lightweight self-correlation module (LSCM) is designed to capture global interactions, with only few computational overheads and network parameters. In neck, LSCM is extended into a lightweight crosscorrelation module (LCCM), capturing mutual dependencies among neighboring scale features. We have conducted exhaustive experiments on MS COCO and Pascal VOC 2007 datasets. The experimental results demonstrate that DPNet achieves state-of the-art trade-off between detection accuracy and implementation efficiency. Specifically, DPNet achieves 30.5% AP on MS COCO test-dev and 81.5% mAP on Pascal VOC 2007 test set, together mwith nearly 2.5M model size, 1.04 GFLOPs, and 164 FPS and 196 FPS for 320 x 320 input images of two datasets.

Related papers

• BAFNet: Bilateral Attention Fusion Network for Lightweight Semantic Segmentation of Urban Remote Sensing Images [6.153725909241752]
  We propose a lightweight semantic segmentation network called bilateral attention fusion network (BAFNet) to efficiently segment high-resolution urban remote sensing images. BAFNet outperforms advanced lightweight models in accuracy but also demonstrates comparable performance to non-lightweight state-of-the-art methods on two datasets.
  arXiv  Detail & Related papers  (2024-09-16T13:25:42Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment [61.45639694373033]
  We propose a novel lightweight network for optical remote sensing images (ORSI-SOD) based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, and a portable decoder for inference.
  arXiv  Detail & Related papers  (2023-01-07T04:33:51Z)
• SALISA: Saliency-based Input Sampling for Efficient Video Object Detection [58.22508131162269]
  We propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for video object detection. We show that SALISA significantly improves the detection of small objects.
  arXiv  Detail & Related papers  (2022-04-05T17:59:51Z)
• Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation [93.80710126516405]
  We propose a novel lightweight ORSI-SOD solution, named CorrNet, to address these issues. By reducing the parameters and computations of each component, CorrNet ends up having only 4.09M parameters and running with 21.09G FLOPs. Experimental results on two public datasets demonstrate that our lightweight CorrNet achieves competitive or even better performance compared with 26 state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-20T08:28:01Z)
• DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention [16.13989397708127]
  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.
  arXiv  Detail & Related papers  (2021-10-31T13:38:16Z)
• Dense Dual-Path Network for Real-time Semantic Segmentation [7.8381744043673045]
  We introduce a novel Dual-Path Network (DDPNet) for real-time semantic segmentation under resource constraints. DDPNet achieves 75.3% mIoU with 52.6 FPS for an input of 1024 X 2048 resolution on a single GTX 1080Ti card.
  arXiv  Detail & Related papers  (2020-10-21T06:11:41Z)
• Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving [94.40107679615618]
  This paper proposes a novel binarized driveable area detection network (binary DAD-Net) It uses only binary weights and activations in the encoder, the bottleneck, and the decoder part. It outperforms state-of-the-art semantic segmentation networks on public datasets.
  arXiv  Detail & Related papers  (2020-06-15T07:09:01Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.