FriendNet：对检测友好的去雾网络
FriendNet: Detection-Friendly Dehazing Network
AI总结：
提出FriendNet架构，通过引导信息和任务驱动学习将图像去雾与目标检测结合，利用去雾特征提升检测性能，同时用检测任务指导去雾过程，在图像质量和检测精度上优于现有方法。
摘要：
恶劣天气条件通常会损害捕获图像的质量，不可避免地影响用于高级驾驶辅助系统（ADAS）和自动驾驶的前沿目标检测模型。在本文中，我们提出了一个有趣的问题：图像恢复与目标检测的结合能否在恶劣天气条件下提升检测性能？为了回答这一问题，我们提出了一种有效的架构，通过引导信息和任务驱动学习将图像去雾与目标检测连接起来，以实现检测友好的去雾，称为FriendNet。FriendNet旨在提供高质量的感知能力和强大的检测能力。与现有将图像去雾直观地视为预处理的工作不同，FriendNet在这两个任务之间建立了正相关关系。去雾网络生成的清晰特征有助于提升目标检测性能。反之，在任务驱动学习方案下，目标检测关键地指导了图像去雾网络的学习过程。我们从网络架构和学习目标两方面阐明了下游任务如何指导上游去雾过程。我们设计了Guidance Fusion Block (GFB) 和 Guidance Attention Block (GAB) 以促进检测信息融入网络。此外，检测任务损失的引入有助于优化优化过程。另外，我们引入了新的Physics-aware Feature Enhancement Block (PFEB)，它集成了基于物理的先验以增强特征提取和表示能力。在合成和真实数据集上的大量实验表明，我们的方法在图像质量和检测精度上均优于现有最先进的方法。源代码可在 https://github.com/fanyihua0309/FriendNet 获取。
英文摘要：
Adverse weather conditions often impair the quality of captured images, inevitably inducing cutting-edge object detection models for advanced driver assistance systems (ADAS) and autonomous driving. In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions? To answer it, we propose an effective architecture that bridges image dehazing and object detection together via guidance information and task-driven learning to achieve detection-friendly dehazing, termed FriendNet. FriendNet aims to deliver both high-quality perception and high detection capacity. Different from existing efforts that intuitively treat image dehazing as pre-processing, FriendNet establishes a positive correlation between these two tasks. Clean features generated by the dehazing network potentially contribute to improvements in object detection performance. Conversely, object detection crucially guides the learning process of the image dehazing network under the task-driven learning scheme. We shed light on how downstream tasks can guide upstream dehazing processes, considering both network architecture and learning objectives. We design Guidance Fusion Block (GFB) and Guidance Attention Block (GAB) to facilitate the integration of detection information into the network. Furthermore, the incorporation of the detection task loss aids in refining the optimization process. Additionally, we introduce a new Physics-aware Feature Enhancement Block (PFEB), which integrates physics-based priors to enhance the feature extraction and representation capabilities. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our method over state-of-the-art methods on both image quality and detection precision. Our source code is available at https://github.com/fanyihua0309/FriendNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
