{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:42:30Z","timestamp":1785436950435,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tianjin Municipal Undergraduate Teaching Quality and Teaching Reform Research Program for Higher Education Institutions","award":["B251006001"],"award-info":[{"award-number":["B251006001"]}]},{"name":"Teaching Fund of Tianjin University of Technology","award":["ZD25-01"],"award-info":[{"award-number":["ZD25-01"]}]},{"award":["ZD25-01"],"award-info":[{"award-number":["ZD25-01"]}],"id":[{"id":"https:\/\/ror.org\/00zbe0w13","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>For sorting mechanisms that operate on a planar platform, the perception module may need to estimate object category, image-plane position, object extent and principal-axis orientation within the computing budget of a low-power device. This paper reports an oriented object detection workflow for a MaixCAM Pro (Shenzhen Sipeed Technology Co., Ltd., Shenzhen, China) waste-sorting prototype. The sensing output is represented by oriented bounding boxes (OBBs), which provide two-dimensional position, rectangular extent and principal-axis orientation for downstream sorting control. To adapt the detector to a small self-built dataset and INT8 deployment, class- and aspect-ratio-aware re-sampling (CAR-RS) repeats selected training images, while class- and aspect-ratio-aware calibration (CAR-Calib) selects a stratified calibration subset for post-training quantization. The YOLO11-OBB model is exported as a static ONNX graph, adapted by exposing the OBB head output tensors, compiled with TPU-MLIR and executed through native MaixPy OBB inference. On the original PC validation split, the baseline and CAR-RS models both obtain 0.995 mAP50, with mAP50\u201395 values of 0.935 and 0.932, respectively. CAR-RS reduces the angle mean absolute error from 3.403\u00b0 to 3.201\u00b0 for objects with aspect ratio greater than or equal to 1.3. On an additional 80-image cross-background test set that was not used for training or calibration, CAR-RS improves precision from 0.839 to 0.907, recall from 0.882 to 0.893 and mAP50 from 0.916 to 0.927, while mAP50\u201395 changes from 0.797 to 0.789. On a 100-image MaixCAM Pro validation set, the CAR-RS + CAR-Calib INT8 model achieves 0.961 precision, 0.994 recall, 0.977 F1-score and 0.980 mAP50. The deployed INT8 model occupies 11.85 MB and runs at 8.40 FPS. The results support the technical feasibility of executing an OBB detector on MaixCAM Pro, while also showing that independent-scene validation remains necessary for broader deployment.<\/jats:p>","DOI":"10.3390\/s26154786","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T08:52:01Z","timestamp":1785228721000},"page":"4786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Embedded Oriented Object Detection on MaixCAM Pro for Waste-Sorting Perception"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9391-2715","authenticated-orcid":false,"given":"Kaihan","family":"Xie","sequence":"first","affiliation":[{"name":"Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruobing","family":"Qin","sequence":"additional","affiliation":[{"name":"Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8234-5732","authenticated-orcid":false,"given":"Jiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China"},{"name":"National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulong","family":"Xing","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17531","DOI":"10.1021\/acs.est.2c05874","article-title":"Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions","volume":"56","author":"Zhao","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6473","DOI":"10.1109\/ACCESS.2024.3350987","article-title":"A Survey of the State of the Art in Sensor-Based Sorting Technology and Research","volume":"12","author":"Maier","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128151","DOI":"10.1109\/ACCESS.2022.3226682","article-title":"A Survey on Waste Detection and Classification Using Deep Learning","volume":"10","author":"Abdu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fotovvatikhah, F., Ahmedy, I., Md Noor, R., and Munir, M.U. (2025). A Systematic Review of AI-Based Techniques for Automated Waste Classification. Sensors, 25.","DOI":"10.3390\/s25103181"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, T., Cai, Y., Liang, L., and Ye, D. (2020). A Multi-Level Approach to Waste Object Segmentation. Sensors, 20.","DOI":"10.3390\/s20143816"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.wasman.2023.02.014","article-title":"Garbage Detection and Classification Using a New Deep Learning-Based Machine Vision System as a Tool for Sustainable Waste Recycling","volume":"162","author":"Jin","year":"2023","journal-title":"Waste Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.wasman.2021.12.001","article-title":"Deep Learning-Based Waste Detection in Natural and Urban Environments","volume":"138","author":"Majchrowska","year":"2022","journal-title":"Waste Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"128601","DOI":"10.1016\/j.jenvman.2026.128601","article-title":"WCD-YOLO: A Waste Classification Detection Model","volume":"398","author":"Ling","year":"2026","journal-title":"J. Environ. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"18151","DOI":"10.1038\/s41598-025-94772-x","article-title":"Advancing E-Waste Classification with Customizable YOLO Based Deep Learning Models","volume":"15","author":"Rajeev","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127525","DOI":"10.1016\/j.eswa.2025.127525","article-title":"GD-YOLO: A Lightweight Model for Household Waste Image Detection","volume":"279","author":"Sun","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11554-025-01655-5","article-title":"YOLO-VG: An Efficient Real-Time Recyclable Waste Detection Network","volume":"22","author":"Song","year":"2025","journal-title":"J. Real-Time Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e70022","DOI":"10.1049\/ipr2.70022","article-title":"DRSE-YOLO: Efficient and Lightweight Architecture for Accurate Waste Detection","volume":"19","author":"Sun","year":"2025","journal-title":"IET Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"113028","DOI":"10.1016\/j.knosys.2025.113028","article-title":"An Automated Waste Classification System Using Deep Learning Techniques: Toward Efficient Waste Recycling and Environmental Sustainability","volume":"310","author":"Nahiduzzaman","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.wasman.2024.12.014","article-title":"AI-Based Plastic Waste Sorting Method Utilizing Object Detection Models for Enhanced Classification","volume":"193","author":"Son","year":"2025","journal-title":"Waste Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ren, Y., Li, Y., and Gao, X. (2024). An MRS-YOLO Model for High-Precision Waste Detection and Classification. Sensors, 24.","DOI":"10.21203\/rs.3.rs-4485704\/v1"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Hashmi, K.A., Pagani, A., Liwicki, M., Stricker, D., and Afzal, M.Z. (2021). Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments. Sensors, 21.","DOI":"10.20944\/preprints202106.0590.v1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Steindl, G., Baca, A., and Kornfeind, P. (2026). Influences and Training Strategies for Effective Object Detection in Challenging Environments Using YOLO NAS-L. Sensors, 26.","DOI":"10.3390\/s26010190"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"119960","DOI":"10.1016\/j.eswa.2023.119960","article-title":"A Comprehensive Survey of Oriented Object Detection in Remote Sensing Images","volume":"224","author":"Wen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object Detection With Deep Learning: A Review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1007\/s10462-025-11256-0","article-title":"Oriented Object Detection in Optical Remote Sensing Images Using Deep Learning: A Survey","volume":"58","author":"Wang","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"129251","DOI":"10.1016\/j.neucom.2024.129251","article-title":"EOOD: End-to-End Oriented Object Detection","volume":"621","author":"Zhang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.isprsjprs.2025.02.025","article-title":"GOOD: Towards Domain Generalized Oriented Object Detection","volume":"223","author":"Bi","year":"2025","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7457","DOI":"10.1109\/JIOT.2020.2984887","article-title":"Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence","volume":"7","author":"Deng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1007\/s11263-022-01622-8","article-title":"A Survey on Long-Tailed Visual Recognition","volume":"130","author":"Yang","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","unstructured":"Sipeed (2026, June 20). MaixPy: Object Detection with YOLO Models. Available online: https:\/\/wiki.sipeed.com\/maixpy\/doc\/en\/vision\/yolov5.html."},{"key":"ref_29","unstructured":"Sipeed (2026, June 29). Using Rotated Bounding Boxes (OBB) in MaixPy MaixCAM. Available online: https:\/\/wiki.sipeed.com\/maixpy\/doc\/en\/vision\/detect_obb.html#Using-Rotated-Bounding-Boxes-%28OBB%29-in-MaixPy-MaixCAM."},{"key":"ref_30","unstructured":"SOPHGO (2026, June 20). TPU-MLIR Toolchain. Available online: https:\/\/github.com\/sophgo\/tpu-mlir."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","article-title":"Pruning and Quantization for Deep Neural Network Acceleration: A Survey","volume":"461","author":"Liang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wei, L., Ma, Z., Yang, C., and Yao, Q. (2024). Advances in the Neural Network Quantization: A Comprehensive Review. Appl. Sci., 14.","DOI":"10.20944\/preprints202407.0076.v1"},{"key":"ref_33","unstructured":"Sipeed (2026, July 16). MaixCAM Pro Hardware Documentation. Available online: https:\/\/wiki.sipeed.com\/hardware\/en\/maixcam\/maixcam_pro.html."},{"key":"ref_34","unstructured":"Ultralytics (2026, June 29). Oriented Bounding Boxes Object Detection. Available online: https:\/\/docs.ultralytics.com\/tasks\/obb."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021). Oriented R-CNN for Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Yang, X., Zhang, G., Wang, J., Liu, Y., Hou, L., Jiang, X., Liu, X., Yan, J., and Lyu, C. (2022). MMRotate: A Rotated Object Detection Benchmark Using PyTorch. Proceedings of the 30th ACM International Conference on Multimedia, ACM.","DOI":"10.1145\/3503161.3548541"},{"key":"ref_39","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 (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.324"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4786\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T08:53:24Z","timestamp":1785228804000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4786"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":39,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26154786"],"URL":"https:\/\/doi.org\/10.3390\/s26154786","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]}}}