Real-time instance segmentation based on contour learning

Instance segmentation plays an important role in image processing. The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end, which can improve the performance of instance segmentation, but has defects such as slow segmentation speed and sub-optimal initial contour. To solve these problems, a real-time instance segmentation algorithm based on contour learning was proposed. Firstly, ShuffleNet V2 was used as backbone network, and the receptive field of the model was expanded by using a 5×5 convolution kernel. Secondly, a lightweight up-sampling module, multi-stage aggregation (MSA), performs residual fusion of multi-layer features, which not only improves segmentation speed, but also extracts effective features more comprehensively. Thirdly, a contour initialization method for network learning was designed, and a global contour feature aggregation mechanism was used to return a coarse contour, which solves the problem of excessive error between manually initialized contour and real contour. Finally, the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour. The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD), Cityscapes and Kins datasets, and the average precision reached 55.8 on the SBD; Compared with Deep Snake, the model parameters were reduced by 87.2%, calculation amount was reduced by 78.3%, and segmentation speed reached 39.8 frame·s^-1when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU. The proposed method can reduce resource consumption, realize instance segmentation tasks quickly and accurately, and therefore is more suitable for embedded platforms with limited resources.