Context-Aware Enhanced Feature Refinement for small object detection with Deformable DETR.

IF 2.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Frontiers in Neurorobotics Pub Date : 2025-06-10 eCollection Date: 2025-01-01 DOI:10.3389/fnbot.2025.1588565

Donghao Shi, Cunbin Zhao, Jianwen Shao, Minjie Feng, Lei Luo, Bing Ouyang, Jiamin Huang

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引用次数: 0

Abstract

Small object detection is a critical task in applications like autonomous driving and ship black smoke detection. While Deformable DETR has advanced small object detection, it faces limitations due to its reliance on CNNs for feature extraction, which restricts global context understanding and results in suboptimal feature representation. Additionally, it struggles with detecting small objects that occupy only a few pixels due to significant size disparities. To overcome these challenges, we propose the Context-Aware Enhanced Feature Refinement Deformable DETR, an improved Deformable DETR network. Our approach introduces Mask Attention in the backbone to improve feature extraction while effectively suppressing irrelevant background information. Furthermore, we propose a Context-Aware Enhanced Feature Refinement Encoder to address the issue of small objects with limited pixel representation. Experimental results demonstrate that our method outperforms the baseline, achieving a 2.1% improvement in mAP.

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上下文感知增强特征细化小对象检测与变形的DETR。

在自动驾驶和船舶黑烟检测等应用中，小物体检测是一项关键任务。虽然Deformable DETR具有先进的小目标检测，但由于其依赖cnn进行特征提取，限制了全局上下文理解并导致次优特征表示，因此面临局限性。此外，由于显著的尺寸差异，它难以检测仅占用几个像素的小物体。为了克服这些挑战，我们提出了上下文感知增强特征细化可变形DETR，这是一种改进的可变形DETR网络。我们的方法在主干中引入了掩模注意，以改进特征提取，同时有效地抑制不相关的背景信息。此外，我们提出了一个上下文感知增强特征细化编码器，以解决像素表示有限的小对象问题。实验结果表明，我们的方法优于基线，实现了2.1%的mAP改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.