MT-TPPNet: Leveraging Decoupled Feature Learning for Generic and Real-Time Multi-Task Network

Transportation panoptic perception (TPP) is a fundamental capability for both on-board and roadside monitoring systems. In this paper, we propose an end-to-end lightweight multitask model, MT-TPPNet, which jointly performs three tasks: object detection, drivable area segmentation, and lane line segmentation. To accommodate task differences while sharing a common backbone, we introduce the Asymmetric Projection with Expanded-value (APEX) mechanism, which integrates attention mechanisms with different biases to enhance performance across various tasks. We further propose the Selective Channel–Spatial Coupling (SC2) mechanism, which injects complementary frequency-band information into the channel-spatial coupled features. Additionally, by using a unified loss function to simultaneously handle detection and segmentation tasks, we eliminate the need for task-specific customizations, improving both training stability and deployment flexibility. Extensive experiments on self-collected field data and public benchmarks from roadway and railway scenarios demonstrate that MT-TPPNet consistently outperforms strong baselines in terms of mAP, mIoU, and FPS. In particular, MT-TPPNet achieves a mAP50 of 83.2% for traffic object detection, a mIoU of 91.6% for drivable-area segmentation, and an IoU of 28.9% for lane-line segmentation, demonstrating the effectiveness of the proposed approach.