Guo Qianren, Wang Yuehang, Zhang Yongji, Zhao Minghao, Jiang Yu
College of Software, Jilin University, Changchun, Jilin, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, Jilin, China.
Sci Prog. 2024 Jul-Sep;107(3):368504241263165. doi: 10.1177/00368504241263165.
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.
视觉目标检测技术的广泛研究与应用极大地改变了自动驾驶行业。自动驾驶严重依赖视觉传感器来感知和分析环境。然而，在暴雨、大雾或低光照等极端天气条件下，这些传感器可能会受到干扰，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。为应对这些挑战，我们提出了自适应图像增强（AIE）-YOLO，这是一种新颖的目标检测方法，用于提高极端天气条件下道路目标的检测精度。为解决极端天气下图像质量退化的问题，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，从而提高目标的可见性并抑制无关背景干扰。此外，我们引入了一个空间特征提取模块，以在复杂背景下自适应增强模型的空间建模能力。此外，还设计了一个通道特征提取模块，以自适应增强模型的表示能力和泛化能力。由于难以获取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-稀有目标数据集的新型基准数据集。该数据集包含十种模拟极端天气场景，并基于一个公开可用的稀有目标检测数据集构建。在极端天气模拟-稀有目标数据集上进行的大量实验表明，AIE-YOLO优于现有的最先进方法，在极端天气条件下实现了出色的检测性能。
