Large scale instance segmentation of outdoor environment based on improved YOLACT

Instance segmentation is a challenging task that requires both instance-level and pixel-level prediction and it has a wide range of applications in autonomous driving, video analysis, scene understandingand so on. The currently dominant instance segmentation methods have excellent accuracy, but they are slow, and the processing speed will be even less satisfactory if the input is a large-scale image. In order to improve the efficiency and accuracy of instance segmentation of large-scale images, this article modifies the backbone network based on YOLACT network, adds a multi-information fusion module and provides an improved BiFPN method to achieve multi-scale feature fusion, while adding two branches to the first level detector RetinaNet to achieve instance segmentation. The network model is tested on Cityscapes dataset and the results of the experiments show that the improved instance segmentation network in this article improves the accuracy while ensuring the speed of segmentation. The optimized network model size was reduced by 17% compared to YOLACT, and the mAP, mAP50, and mAP75 were improved by 18.3%, 32.1%, and 24.6%, respectively.

Automated summary

This article proposes an improved instance segmentation method that improves the efficiency of processing large-scale images while ensuring accuracy, and its effectiveness is demonstrated in experiments.