The existing vehicle environment perception systems remain limited with regard to the ability to detect objects under complex weather conditions. This study proposes a novel network architecture named multi-weather network (MWNet), which can improve the performance of the on-board object detection system under extreme weather conditions. It consists of an encoder and a decoder. The encoder is comprised of shared convolutional layers used to extract features, while the decoder consists of three subnets, namely weather classification subnet, bad weather detection subnet, and fair weather detection subnet. Moreover, the results are satisfactory even for images photographed under different weather and illumination conditions.
MWNet: object detection network applicable for different weather conditions
IET Intelligent Transport Systems ; 13 , 9 ; 1394-1400
2019-06-10
7 pages
Article (Journal)
Electronic Resource
English
object detection , weather conditions , complex weather conditions , object detection network , bad weather detection subnet , MWNet , weather classification subnet , encoder , decoder , novel network architecture , multiweather network , fair weather detection subnet , vehicle environment perception systems , shared convolutional layers , traffic engineering computing , extreme weather conditions , feature extraction , illumination conditions , on-board object detection system
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