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
2019-09-01
7 pages
Article (Journal)
Electronic Resource
English
MWNet , extreme weather conditions , illumination conditions , decoder , shared convolutional layers , complex weather conditions , object detection , object detection network , novel network architecture , multiweather network , weather conditions , vehicle environment perception systems , bad weather detection subnet , traffic engineering computing , feature extraction , weather classification subnet , encoder , fair weather detection subnet , on‐board object detection system
MWNet: object detection network applicable for different weather conditions
IET | 2019
|Analysis of Object Detection Under Different Weather Conditions in Simulated and Real Environment
Springer Verlag | 2022
|Robust 3D Object Detection in Cold Weather Conditions
IEEE | 2022
|