Automated Generation of Instance Segmentation Labels for Traffic Surveillance Models

This paper focuses on instance segmentation and object detection for real-time traffic surveillance applications. Although instance segmentation is currently a hot topic in literature, no suitable dataset for traffic surveillance applications is publicly available and limited work is available with real-time performance. A custom proprietary dataset is available for training, but it contains only bounding-box annotations and lacks segmentation annotations. The paper explores methods for automated generation of instance segmentation labels for custom datasets that can be utilized to finetune state-of-the-art segmentation models to specific application domains. Real-time performance is obtained by adopting the recent YOLACT instance segmentation with the YOLOv7 backbone. Nevertheless, it requires modification of the loss function and an implementation of ground-truth matching to overcome handling imperfect instance labels in custom datasets. Experiments show that it is possible to achieve a high instance segmentation performance using a semi-automatically generated dataset, especially when using the Segment Anything Model for generating the labels.