{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:58:19Z","timestamp":1790657899040,"version":"4.1.0"},"reference-count":36,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["ALLRP 580846 - 22"],"award-info":[{"award-number":["ALLRP 580846 - 22"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"award":["ALLRP 580846 - 22"],"award-info":[{"award-number":["ALLRP 580846 - 22"]}],"id":[{"id":"https:\/\/ror.org\/01h531d29","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Millimeter-wave radar is an attractive sensing modality for autonomous driving because of its robustness under adverse weather and lighting conditions. However, comparisons between radar object detectors are hindered by inconsistent evaluation procedures and limited reporting of deployment costs. This work presents a comprehensive benchmark using standardized VOC mean average precision (mAP) and evaluates processing-pipeline latency across preprocessing, network inference, and post-processing. We evaluate SSRaDNet, a lightweight hybrid CNN\u2013Swin Transformer detector, across analog-to-digital converter (ADC), range-Doppler (RD), and range-azimuth-Doppler (RAD) representations and compare it with existing methods on RADIal and RADDet. SSRaDNet achieves state-of-the-art AP@0.5 among re-evaluated RADIal models using RD input, reaching 88.89%, and provides the strongest overall balance of detection and segmentation performance among the compared ADC configurations. On RADDet, it achieves the best performance among methods using the native-resolution RAD tensor (45.69% mAP@0.5) and introduces the first ADC-based detector with full bounding-box regression. We also evaluate alternative post-processing strategies, including hybrid MaxPoolNMS\u2013GreedyNMS, to characterize accuracy\u2013latency trade-offs associated with alternative post-processing strategies. These results demonstrate the importance of standardized evaluation and full-pipeline runtime analysis for radar-based object detection.<\/jats:p>","DOI":"10.3390\/s26196074","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T08:09:02Z","timestamp":1790323742000},"page":"6074","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SSRaDNet: An Empirical Study of Radar Object Detection Evaluation and Deployment Trade-Offs"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8794-3024","authenticated-orcid":false,"given":"Iqbal","family":"Banwait","sequence":"first","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7865-1944","authenticated-orcid":false,"given":"Niclas","family":"Zeller","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, Karlsruhe University of Applied Sciences, Moltkestra\u00dfe 30, 76133 Karlsruhe, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7129-4825","authenticated-orcid":false,"given":"Javad","family":"Alirezaie","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rebut, J., Ouaknine, A., Malik, W., and P\u00e9rez, P. (2022). Raw High-Definition Radar for Multi-Task Learning. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01651"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Donnet, B., and Longstaff, I. (2006). MIMO radar, techniques and opportunities. Proceedings of the 2006 European Radar Conference, IEEE.","DOI":"10.1109\/EURAD.2006.280286"},{"key":"ref_3","unstructured":"Nabati, R., and Qi, H. (2020). Radar-camera sensor fusion for joint object detection and distance estimation in autonomous vehicles. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, T., Anklam, C., and Goldluecke, B. (2020). High dimensional frustum pointnet for 3d object detection from camera, lidar, and radar. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), IEEE.","DOI":"10.1109\/IV47402.2020.9304655"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, B., Guo, R., Liang, M., Casas, S., and Urtasun, R. (2020). Radarnet: Exploiting radar for robust perception of dynamic objects. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58523-5_29"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nabati, R., and Qi, H. (2021). Centerfusion: Center-based radar and camera fusion for 3d object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, IEEE.","DOI":"10.1109\/WACV48630.2021.00157"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, A., Nowruzi, F.E., and Lagani\u00e8re, R. (2021). RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users. arXiv.","DOI":"10.1109\/CRV52889.2021.00021"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TRS.2025.3537604","article-title":"Transrad: Retentive vision transformer for enhanced radar object detection","volume":"3","author":"Cheng","year":"2025","journal-title":"IEEE Trans. Radar Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Giroux, J., Bouchard, M., and Laganiere, R. (2023). T-fftradnet: Object detection with swin vision transformers from raw adc radar signals. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCVW60793.2023.00435"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Decourt, C., VanRullen, R., Salle, D., and Oberlin, T. (2022). DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps. Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV), IEEE.","DOI":"10.1109\/IV51971.2022.9827281"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5715","DOI":"10.1109\/TVT.2022.3232135","article-title":"YOLO-ORE: A deep learning-aided object recognition approach for radar systems","volume":"72","author":"Huang","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Lee, M.C., and Lee, T.S. (2022). Deep-learning based multi-object detection and tracking using range-angle map in automotive radar systems. Proceedings of the 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE.","DOI":"10.1109\/VTC2022-Spring54318.2022.9860725"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5119","DOI":"10.1109\/JSEN.2020.3036047","article-title":"Ramp-CNN: A novel neural network for enhanced automotive radar object recognition","volume":"21","author":"Gao","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hu, K., Hu, X., Qi, L., Lu, G., Zhong, Y., and Han, Y. (2023). Radnet: A radar detection network for target detection using 3d range-angle-doppler tensor. Proceedings of the 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), IEEE.","DOI":"10.1109\/CASE56687.2023.10260345"},{"key":"ref_15","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zou, H., Xie, Z., Ou, J., and Gao, Y. (2023). Transrss: Transformer-based radar semantic segmentation. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA48891.2023.10161200"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3012","DOI":"10.1109\/TIV.2023.3234583","article-title":"Cross-modal supervision-based multitask learning with automotive radar raw data","volume":"8","author":"Jin","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/JSTSP.2021.3058895","article-title":"RODNet: A real-time radar object detection network cross-supervised by camera-radar fused object 3D localization","volume":"15","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ouaknine, A., Newson, A., P\u00e9rez, P., Tupin, F., and Rebut, J. (2021). Multi-view radar semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.01538"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ju, B., Yang, W., Jia, J., Ye, X., Chen, Q., Tan, X., Sun, H., Shi, Y., and Ding, E. (2021). Danet: Dimension apart network for radar object detection. Proceedings of the Proceedings of the 2021 International Conference on Multimedia Retrieval, Association for Computing Machinery.","DOI":"10.1145\/3460426.3463656"},{"key":"ref_21","first-page":"5003912","article-title":"T-RODNet: Transformer for Vehicular Millimeter-Wave Radar Object Detection","volume":"72","author":"Jiang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. arXiv.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fan, Q., Huang, H., Chen, M., Liu, H., and He, R. (2023). Rmt: Retentive networks meet vision transformers. arXiv.","DOI":"10.1109\/CVPR52733.2024.00539"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dalbah, Y., Lahoud, J., and Cholakkal, H. (2023). RadarFormer: Lightweight and accurate real-time radar object detection model. Proceedings of the Scandinavian Conference on Image Analysis, Springer.","DOI":"10.1007\/978-3-031-31435-3_23"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Banwait, I., Zeller, N., and Alirezaie, J. (2025). CNN-Swin Backbones in Radar Object Detection for Autonomous Vehicles using Raw ADC Signals. Proceedings of the 2025 21st International Conference on Intelligent Environments (IE), IEEE.","DOI":"10.1109\/IE64880.2025.11130063"},{"key":"ref_27","unstructured":"Banwait, I., Alirezaie, J., and Zeller, N. (September, January 31). Single-Frame Radar Object Detection and Segmentation Using a CNN\u2013Swin Hybrid Architecture. Proceedings of the 34th European Signal Processing Conference (EUSIPCO), Bruges, Belgium."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1109\/JSTSP.2021.3061270","article-title":"RaDICaL: A Synchronized FMCW Radar, Depth, IMU and RGB Camera Data Dataset With Low-Level FMCW Radar Signals","volume":"15","author":"Lim","year":"2021","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nowruzi, F.E., Kolhatkar, D., Kapoor, P., Al Hassanat, F., Heravi, E.J., Laganiere, R., Rebut, J., and Malik, W. (2020). Deep Open Space Segmentation using Automotive Radar. Proceedings of the 2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), IEEE.","DOI":"10.1109\/ICMIM48759.2020.9299052"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10236","DOI":"10.1109\/JIOT.2023.3237494","article-title":"Cubelearn: End-to-end learning for human motion recognition from raw mmwave radar signals","volume":"10","author":"Zhao","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, B., Luo, W., and Urtasun, R. (2018). Pixor: Real-time 3d object detection from point clouds. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00798"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_35","unstructured":"Texas Instruments (2026, September 11). AWR1843 Single-Chip 77 to 79 GHz FMCW Radar Sensor, 2024. Rev. D, Revised September 2024. Available online: https:\/\/www.ti.com\/lit\/ds\/symlink\/awr1843.pdf?ts=1590562743048&utm_source=everythingrf."},{"key":"ref_36","unstructured":"NXP Semiconductors (2026, September 11). S32R45 Radar Processor: High-Performance, Safe and Secure Processing for Long-Range Radar Imaging; S32R45FS, Rev. 2, 2020. Available online: https:\/\/www.nxp.com\/docs\/en\/fact-sheet\/S32R45FS.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6074\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:25:13Z","timestamp":1790655913000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"references-count":36,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196074"],"URL":"https:\/\/doi.org\/10.3390\/s26196074","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]}}}