{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T13:46:35Z","timestamp":1776433595189,"version":"3.51.2"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Major Research Instrument Development Project","award":["62127813"],"award-info":[{"award-number":["62127813"]}]},{"name":"National Major Research Instrument Development Project","award":["20210203181SF"],"award-info":[{"award-number":["20210203181SF"]}]},{"name":"Jilin Provincial Science and Technology Department Development Project","award":["62127813"],"award-info":[{"award-number":["62127813"]}]},{"name":"Jilin Provincial Science and Technology Department Development Project","award":["20210203181SF"],"award-info":[{"award-number":["20210203181SF"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The importance of panoramic traffic perception tasks in autonomous driving is increasing, so shared networks with high accuracy are becoming increasingly important. In this paper, we propose a multi-task shared sensing network, called CenterPNets, that can perform the three major detection tasks of target detection, driving area segmentation, and lane detection in traffic sensing in one go and propose several key optimizations to improve the overall detection performance. First, this paper proposes an efficient detection head and segmentation head based on a shared path aggregation network to improve the overall reuse rate of CenterPNets and an efficient multi-task joint training loss function to optimize the model. Secondly, the detection head branch uses an anchor-free frame mechanism to automatically regress target location information to improve the inference speed of the model. Finally, the split-head branch fuses deep multi-scale features with shallow fine-grained features, ensuring that the extracted features are rich in detail. CenterPNets achieves an average detection accuracy of 75.8% on the publicly available large-scale Berkeley DeepDrive dataset, with an intersection ratio of 92.8% and 32.1% for driveableareas and lane areas, respectively. Therefore, CenterPNets is a precise and effective solution to the multi-tasking detection issue.<\/jats:p>","DOI":"10.3390\/s23052467","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T04:32:33Z","timestamp":1677126753000},"page":"2467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["CenterPNets: A Multi-Task Shared Network for Traffic Perception"],"prefix":"10.3390","volume":"23","author":[{"given":"Guangqiu","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Chang Chun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Chang Chun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Chang Chun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Chang Chun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dandan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Chang Chun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Chang Chun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). SSD: Single Shot MultiBox Detector. InComputer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46478-7"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_5","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architec-ture for real-time semantic segmentation. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1134\/S1054661819040126","article-title":"Strong-Structural Convolution Neural Network for Semantic Segmentation","volume":"29","author":"Ouyang","year":"2019","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_7","unstructured":"Wang, Z., Ren, W., and Qiu, Q. (2018). LaneNet: Real-Time Lane Detection Networks for AutonomousDriving. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pan, X., Shi, J., Luo, P., Wang, X., and Tang, X. (2017). Spatial As Deep: Spatial CNN for Traffic SceneUndrstanding. arXiv.","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"ref_9","unstructured":"Hou, Y., Ma, Z., Liu, C., and Loy, C.C. (November, January 27). Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Proceedings of the IEEE\/CVFInternationalConference onComputer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE Trans. PatternAnal. Mach. Intell."},{"key":"ref_11","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with regionproposalnetworks. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, B., Chen, H., and Wang, Z. (2022). LSNet: Extremely Light-Weight Siamese Network For ChangeDetection in Remote Sensing Image. arXiv.","DOI":"10.1109\/IGARSS46834.2022.9884446"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Teichmann, M., Weber, M., Z\u00f6llner, M., Cipolla, R., and Urtasun, R. (2018, January 26\u201330). MultiNet: Real-time JointSemantic Reasoning for Autonomous Driving. Proceedings of the IEEE IntelligentVehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500504"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020, January 13\u201319). BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, D., Liao, M., Zhang, W., and Wang, X. (2022). YOLOP: You Only Look Once for Panoptic DrivingPerception. arXiv.","DOI":"10.1007\/s11633-022-1339-y"},{"key":"ref_16","unstructured":"Vu, D., Ngo, B., and Phan, H. (2022). HybridNets: End-to-End Perception Network. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.Y.M. (2020). Scaled-yolov4: Scaling cross stage partialnetwork. arXiv.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR);IEEE: 2016. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks forvisual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., and Belongie, S. (2017, January 21\u201326). Feature pyramidnetworks for object detection. Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_22","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). Cornernet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y. (2017, January 21\u201326). Realtime multi-person 2d poseestimation using part affinity fields. Proceedings of the IEEE Conferenceon Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for denseobject detection. Proceedings of the IEEE international Conference onComputer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Q., Shi, Y., Suk, H.I., and Suzuki, K. (2017). Machine Learning in Medical Imaging, MLMI 2017, Springer. Lecture Notes in Computer, Science.","DOI":"10.1007\/978-3-319-67389-9"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense ObjectDetection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","unstructured":"Loshchilov, I., and Hutter, F. (2019). Decoupled Weight Decay Regularization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:40:19Z","timestamp":1760121619000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":28,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052467"],"URL":"https:\/\/doi.org\/10.3390\/s23052467","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]}}}