{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:45:20Z","timestamp":1765190720033,"version":"3.46.0"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Quzhou City Science and Technology Plan Project","award":["2024K176"],"award-info":[{"award-number":["2024K176"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2013Spatial 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.<\/jats:p>","DOI":"10.3390\/computers14120536","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:35:51Z","timestamp":1765190151000},"page":"536","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MT-TPPNet: Leveraging Decoupled Feature Learning for Generic and Real-Time Multi-Task Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7189-8395","authenticated-orcid":false,"given":"Xiaokun","family":"Tang","sequence":"first","affiliation":[{"name":"School of Science, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Science, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohua","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Quzhou College of Technology, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ga\u0161par\u00edk, J., Bulkov\u00e1, Z., and Ded\u00edk, M. 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