Improve the scientific level of the goose breeding industry and help the development of intelligent agriculture. Instance Segmentation has a pivotal role when the breeders make decisions about geese breeding. It can be used for disease prevention, body size estimation and behavioural prediction, etc. However, instance segmentation requires high performance computing devices to run smoothly due to its rich output. To ameliorate this problem, this paper constructs a novel encoder-decoder module and proposes the SDSCNet model. The reasonable use of depth-separable convolution in the module reduces the number and size of model parameters and increase execution speed. Finally, SDSCNet model enables real-time identification and segmentation of individual geese with the accuracy reached 0.933.We compare this model with numerous mainstream instance segmentation models, and the final results demonstrate the excellent performance of our model.Furthermore, deploying SDSCNet model on the embedded device Raspberry Pi 4 Model B can achieve effective detection of continuous moving scenes.