Kavli Affiliate: Feng Yuan
| First 5 Authors: Yaxin Feng, Yuan Lan, Luchan Zhang, Yang Xiang,
| Summary:
Segmentation is a pixel-level classification of images. The accuracy and fast
inference speed of image segmentation are crucial for autonomous driving
safety. Fine and complex geometric objects are the most difficult but important
recognition targets in traffic scene, such as pedestrians, traffic signs and
lanes. In this paper, a simple and efficient geometry-sensitive energy-based
loss function is proposed to Convolutional Neural Network (CNN) for multi-class
segmentation on real-time traffic scene understanding. To be specific, the
elastic interaction energy (EIE) between two boundaries will drive the
prediction moving toward the ground truth until completely overlap. The EIE
loss function is incorporated into CNN to enhance accuracy on fine-scale
structure segmentation. In particular, small or irregularly shaped objects can
be identified more accurately, and discontinuity issues on slender objects can
be improved. Our approach can be applied to different segmentation-based
problems, such as urban scene segmentation and lane detection. We
quantitatively and qualitatively analyze our method on three traffic datasets,
including urban scene data Cityscapes, lane data TuSimple and CULane. The
results show that our approach consistently improves performance, especially
when using real-time, lightweight networks as the backbones, which is more
suitable for autonomous driving.
| Search Query: ArXiv Query: search_query=au:”Feng Yuan”&id_list=&start=0&max_results=3