Adverse weather conditions including haze, snow and rain lead to decline in
image qualities, which often causes a decline in performance for deep-learning
based detection networks. Most existing approaches attempts to rectify hazy
images before performing object detection, which increases the complexity of
the network and may result in the loss in latent information. To better
integrate image restoration and object detection tasks, we designed a
double-route network with an attention feature fusion module, taking both hazy
and dehazed features into consideration. We also proposed a subnetwork to
provide haze-free features to the detection network. Specifically, our D-YOLO
improves the performance of the detection network by minimizing the distance
between the clear feature extraction subnetwork and detection network.
Experiments on RTTS and FoggyCityscapes datasets show that D-YOLO demonstrates
better performance compared to the state-of-the-art methods. It is a robust
detection framework for bridging the gap between low-level dehazing and
high-level detection.

通过设计双路网络和注意力特征融合模块，并提出一个子网络来为检测网络提供无霾特征，我们的 D-YOLO 通过减小清晰特征提取子网络与检测网络之间的距离来改善检测网络的性能，实验证明 D-YOLO 相比现有方法具有更优异的性能表现，是一个强大的检测框架用于弥合低级去雾和高级检测之间的差距。