Query-Based Instance Segmentation with Dual Attention Transformer for Autonomous Vehicles

Applications such as autonomous driving demand real-time and high-precision instance segmentation to accurately identify and understand objects in an environment, including pedestrians, vehicles, and traffic signs. Ensuring a balance between accuracy and efficiency in instance segmentation systems is critical for such tasks. Traditional convolutional models face limitations in capturing complex features and global context effectively. To address these challenges, we propose an enhanced QueryInst-based instance segmentation framework. First, we replace the traditional CNN backbone with the DaViT Transformer to extract richer, multi-scale features. Next, we integrate Feature Pyramid Network CARAFE to capture global context and recover missed instances. Finally, we incorporate the Complete IoU (CIoU) loss function to optimize object localization and improve prediction accuracy. Experiments on the Cityscapes and COCO datasets demonstrate that our approach achieves mIoU scores of 46.7% and AP score of 45.5%, representing improvements of 6.1% and 2.6% over the baseline, respectively, outperforming other state-of-the-art methods.