FriendNet: Detection-Friendly Dehazing Network
Yihua Fan Yongzhen Wang Mingqiang Wei Fu Lee Wang Haoran Xie
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.