Open AccessA two‐scaled fully convolutional learning network for road detection
Author(s) -
Yu Dingding,
Hu Xianliang,
Liang Kewei
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12157
Subject(s) - computer science , pooling , redundancy (engineering) , convolutional neural network , artificial intelligence , fuse (electrical) , deep learning , feature (linguistics) , benchmark (surveying) , pattern recognition (psychology) , layer (electronics) , subnetwork , feature learning , machine learning , data mining , cartography , engineering , linguistics , philosophy , chemistry , computer security , organic chemistry , geography , electrical engineering , operating system
This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance.