AIE-YOLO:通过自适应图像增强在极端驾驶场景中有效检测物体的方法。

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Science Progress Pub Date : 2024-07-01 DOI:10.1177/00368504241263165
Qianren Guo, Yuehang Wang, Yongji Zhang, Minghao Zhao, Yu Jiang
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引用次数: 0

摘要

视觉物体检测技术的广泛研究和应用极大地改变了自动驾驶行业。自动驾驶主要依靠视觉传感器来感知和分析环境。然而,在大雨、大雾或弱光等极端天气条件下,这些传感器可能会遇到干扰,导致图像质量下降和检测精度降低,从而增加自动驾驶的风险。为了应对这些挑战,我们提出了自适应图像增强(AIE)-YOLO--一种新的物体检测方法,以提高极端天气条件下的道路物体检测精度。为了解决极端天气下图像质量下降的问题,我们设计了一个改进的自适应图像增强模块。该模块可根据不同的场景条件动态调整道路图像的像素特征,从而提高物体的可见度并抑制无关的背景干扰。此外,我们还引入了空间特征提取模块,以自适应地增强模型在复杂背景下的空间建模能力。此外,我们还设计了一个信道特征提取模块,以自适应地增强模型的表示和泛化能力。由于难以获得各种极端天气条件下的真实世界数据,我们构建了一个名为 "极端天气模拟-稀有物体数据集 "的新型基准数据集。该数据集包括十种模拟极端天气场景,并建立在一个公开的稀有物体检测数据集之上。在极端天气模拟-稀有物体数据集上进行的大量实验表明,AIE-YOLO 的性能优于现有的先进方法,在极端天气条件下实现了出色的检测性能。
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AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement.

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

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来源期刊
Science Progress
Science Progress Multidisciplinary-Multidisciplinary
CiteScore
3.80
自引率
0.00%
发文量
119
期刊介绍: Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.
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