Contour-based instance segmentation method of road scene

In autonomous driving, instance segmentation is crucial for detecting and segmenting pedestrians and vehicles in road scenes. However, autonomous driving technology faces complex and diverse scene information, and the existing contour-based methods have imprecise initial contours with significant errors. The subsequent contour deformation modules cannot correct errors from the previous iterations, increasing the learning difficulty. Therefore, this research proposes two novel methods: Contour Initialization based on Instance Center Features (CIICF) and Contour Deformation based on Differentiation Module (CDDM). CIICF leverages instance center features to predict distances between instance contours and centers, thereby enhancing the initial contour representation's accuracy. And CDDM substitutes circular convolution with zero-padded one-dimensional convolution which allows contour points to inherently learn their positions relative to the entire contour. Additionally, we incorporate absolute position encoding into the feature map to improve the model's positional awareness. The superiority of our method was validated on public datasets such as Cityscapes, KINS, and SBD. Compared to the baseline CIICF-Deep Snake model, the final contour [Formula: see text], [Formula: see text], [Formula: see text]increased by 6.9%, 5.1% and 9.1% respectively. Moreover, the final contour generation speed enhanced from 43.2 Frames Per Second (FPS) to 49.8 FPS.