Small object detection based on hierarchical attention mechanism and multi‐scale separable detection

Author:

Zhang Yafeng1,Yu Junyang1,Wang Yuanyuan2,Tang Shuang3ORCID,Li Han3,Xin Zhiyi1,Wang Chaoyi4,Zhao Ziming1

Affiliation:

1. School of Software Henan University Kaifeng Henan China

2. Economic and Technical Research Institute State Grid Henan Province Electric Power Company Zhengzhou Henan China

3. School of Economics Henan University Kaifeng Henan China

4. Electrical and Computer Engineering Shanghai Institute of Microsystem and Information Technology Shanghai China

Abstract

AbstractThe ability of modern detectors to detect small targets is still an unresolved topic compared to their capability of detecting medium and large targets in the field of object detection. Accurately detecting and identifying small objects in the real‐world scenario suffer from sub‐optimal performance due to various factors such as small target size, complex background, variability in illumination, occlusions, and target distortion. Here, a small object detection method for complex traffic scenarios named deformable local and global attention (DLGADet) is proposed, which seamlessly merges the ability of hierarchical attention mechanisms (HAMs) with the versatility of deformable multi‐scale feature fusion, effectively improving recognition and detection performance. First, DLGADet introduces the combination of multi‐scale separable detection and multi‐scale feature fusion mechanism to obtain richer contextual information for feature fusion while solving the misalignment problem of classification and localisation tasks. Second, a deformation feature extraction module (DFEM) is designed to address the deformation of objects. Finally, a HAM combining global and local attention mechanisms is designed to obtain discriminative features from complex backgrounds. Extensive experiments on three datasets demonstrate the effectiveness of the proposed methods. Code is available at https://github.com/ACAMPUS/DLGADet

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3