  Haze removal is an extremely challenging task, and object detection in the
hazy environment has recently gained much attention due to the popularity of
autonomous driving and traffic surveillance. In this work, the authors propose
a multiple linear regression haze removal model based on a widely adopted
dehazing algorithm named Dark Channel Prior. Training this model with a
synthetic hazy dataset, the proposed model can reduce the unanticipated
deviations generated from the rough estimations of transmission map and
atmospheric light in Dark Channel Prior. To increase object detection accuracy
in the hazy environment, the authors further present an algorithm to build a
synthetic hazy COCO training dataset by generating the artificial haze to the
MS COCO training dataset. The experimental results demonstrate that the
proposed model obtains higher image quality and shares more similarity with
ground truth images than most conventional pixel-based dehazing algorithms and
neural network based haze-removal models. The authors also evaluate the mean
average precision of Mask R-CNN when training the network with synthetic hazy
COCO training dataset and preprocessing test hazy dataset by removing the haze
with the proposed dehazing model. It turns out that both approaches can
increase the object detection accuracy significantly and outperform most
existing object detection models over hazy images.
