ADNet: Lane Shape Prediction via Anchor Decomposition
Lingyu Xiao Xiang Li Sen Yang Wankou Yang
In this paper, we revisit the limitations of anchor-based lane detection
methods, which have predominantly focused on fixed anchors that stem from the
edges of the image, disregarding their versatility and quality. To overcome the
inflexibility of anchors, we decompose them into learning the heat map of
starting points and their associated directions. This decomposition removes the
limitations on the starting point of anchors, making our algorithm adaptable to
different lane types in various datasets. To enhance the quality of anchors, we
introduce the Large Kernel Attention (LKA) for Feature Pyramid Network (FPN).
This significantly increases the receptive field, which is crucial in capturing
the sufficient context as lane lines typically run throughout the entire image.
We have named our proposed system the Anchor Decomposition Network (ADNet).
Additionally, we propose the General Lane IoU (GLIoU) loss, which significantly
improves the performance of ADNet in complex scenarios. Experimental results on
three widely used lane detection benchmarks, VIL-100, CULane, and TuSimple,
demonstrate that our approach outperforms the state-of-the-art methods on
VIL-100 and exhibits competitive accuracy on CULane and TuSimple. Code and
models will be released on https://github.com/ Sephirex-X/ADNet.