Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …

Adverse weather conditions such as haze and rain corrupt the quality of captured images,
which cause detection networks trained on clean images to perform poorly on these …

In the past half of the decade, object detection approaches based on the convolutional
neural network have been widely studied and successfully applied in many computer vision …

Recently, the deep learning-based object detection methods have achieved a great
success. However, the performance of such techniques deteriorates on the images captured …

Most state-of-the-art object detection methods have achieved impressive perfomrace on
several public benchmarks, which are trained with high definition images. However, existing …

The computer vision systems driving autonomous vehicles are judged by their ability to
detect objects and obstacles in the vicinity of the vehicle in diverse environments. Enhancing …

The ability to detect objects regardless of image distortions or weather conditions is crucial
for real-world applications of deep learning like autonomous driving. We here provide an …

We address the task of domain adaptation in object detection, where there is a domain gap
between a domain with annotations (source) and a domain of interest without annotations …

Though deep learning-based object detection methods have achieved promising results on
the conventional datasets, it is still challenging to locate objects from the low-quality images …

With the rapid development of deep learning in recent years, the level of automatic driving
perception has also increased substantially. However, automatic driving perception under …