{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:29:09Z","timestamp":1760236149916,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Instituto de Engenharia de Sistemas e Computadores Investiga\u00e7\u00e3o e Desenvolvimento","award":["UIDB\/50021\/2020"],"award-info":[{"award-number":["UIDB\/50021\/2020"]}]},{"name":"Instituto Polit\u00e9cnico de Lisboa","award":["IPL\/IDI&CA\/2020\/TRAINEE\/ISEL"],"award-info":[{"award-number":["IPL\/IDI&CA\/2020\/TRAINEE\/ISEL"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Object detection is an important task for many applications, like transportation, security, and medical applications. Many of these applications are needed on edge devices to make local decisions. Therefore, it is necessary to provide low-cost, fast solutions for object detection. This work proposes a configurable hardware core on a field-programmable gate array (FPGA) for object detection. The configurability of the core allows its deployment on target devices with diverse hardware resources. The object detection accelerator is based on YOLO, for its good accuracy at moderate computational complexity. The solution was applied to the design of a core to accelerate the Tiny-YOLOv3, based on a CNN developed for constrained environments. However, it can be applied to other YOLO versions. The core was integrated into a full system-on-chip solution and tested with the COCO dataset. It achieved a performance from 7 to 14 FPS in a low-cost ZYNQ7020 FPGA, depending on the quantization, with an accuracy reduction from 2.1 to 1.4 points of mAP50.<\/jats:p>","DOI":"10.3390\/fi13110280","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:21:08Z","timestamp":1635805268000},"page":"280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Configurable Hardware Core for IoT Object Detection"],"prefix":"10.3390","volume":"13","author":[{"given":"Pedro R.","family":"Miranda","sequence":"first","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal"}]},{"given":"Daniel","family":"Pestana","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8903-9715","authenticated-orcid":false,"given":"Jo\u00e3o D.","family":"Lopes","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7060-4745","authenticated-orcid":false,"given":"Rui Policarpo","family":"Duarte","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8556-4507","authenticated-orcid":false,"given":"M\u00e1rio P.","family":"V\u00e9stias","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, 1500-310 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3621-8322","authenticated-orcid":false,"given":"Hor\u00e1cio C.","family":"Neto","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7525-7546","authenticated-orcid":false,"given":"Jos\u00e9 T.","family":"de Sousa","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","article-title":"A Survey of Deep Learning-Based Object Detection","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep Learning for Generic Object Detection: A Survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s41074-019-0059-x","article-title":"Deep learning-based strategies for the detection and tracking of drones using several cameras","volume":"11","author":"Unlu","year":"2019","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_4","first-page":"23","article-title":"Self-Driving Cars: Evaluation of Deep Learning Techniques for Object Detection in Different Driving Conditions","volume":"2","author":"Simhambhatla","year":"2019","journal-title":"SMU Data Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Merenda, M., Porcaro, C., and Iero, D. (2020). Edge Machine Learning for AI-Enabled IoT Devices: A Review. Sensors, 20.","DOI":"10.3390\/s20092533"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mahony, N.O., Campbell, S., Carvalho, A., Harapanahalli, S., Velasco-Hern\u00e1ndez, G.A., Krpalkova, L., Riordan, D., and Walsh, J. (2019). Deep Learning vs. Traditional Computer Vision. CoRR. arXiv.","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"ref_7","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 Networks Learn. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_10","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2017, January 4\u20139). R-FCN: Object Detection via Region-Based Fully Convolutional Networks. Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS\u201916), Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_13","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_15","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_16","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Carranza-Garc\u00eda, M., Torres-Mateo, J., Lara-Ben\u00edtez, P., and Garc\u00eda-Guti\u00e9rrez, J. (2021). On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera 621 Data. Remote Sens., 13.","DOI":"10.3390\/rs13010089"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"V\u00e9stias, M.P., Duarte, R.P., de Sousa, J.T., and Neto, H.C. (2020). Moving Deep Learning to the Edge. Algorithms, 13.","DOI":"10.3390\/a13050125"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.vlsi.2019.07.005","article-title":"Computer vision algorithms and hardware implementations: A survey","volume":"69","author":"Feng","year":"2019","journal-title":"Integration"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hochberger, C., Nelson, B., Koch, A., Woods, R., and Diniz, P. (2019). Exploring Data Size to Run Convolutional Neural Networks in Low Density FPGAs. Applied Reconfigurable Computing, Springer International Publishing.","DOI":"10.1007\/978-3-030-17227-5"},{"key":"ref_24","unstructured":"Ma, Y., Suda, N., Cao, Y., Seo, J., and Vrudhula, S. (September, January 29). Scalable and modularized RTL compilation 637 of Convolutional Neural Networks onto FPGA. Proceedings of the 26th International Conference on Field Programmable Logic and Applications (FPL), Lausanne, Switzerland."},{"key":"ref_25","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems\u2014Volume 1 (NIPS\u201912), Curran Associates Inc."},{"key":"ref_26","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network In Network. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nguyen, N.T., Jearanaitanakij, K., Selamat, A., Trawi\u0144ski, B., and Chittayasothorn, S. (2020). Post-training Quantization Methods for Deep Learning Models. Intelligent Information and Database Systems, Springer International Publishing.","DOI":"10.1007\/978-3-030-41964-6"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"V\u00e9stias, M.P. (2019). A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing. Algorithms, 12.","DOI":"10.3390\/a12080154"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., and Song, S. (2016, January 21\u201323). Going Deeper with Embedded FPGA Platform for Convolutional Neural Network. Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/2847263.2847265"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., and Cong, J. (2015, January 22\u201324). Optimizing FPGA-Based Accelerator Design for Deep Convolutional Neural Networks. Proceedings of the 2015 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/2684746.2689060"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Suda, N., Chandra, V., Dasika, G., Mohanty, A., Ma, Y., Vrudhula, S., Seo, J.s., and Cao, Y. (2016, January 21\u201323). Throughput-Optimized OpenCL-Based FPGA Accelerator for Large-Scale Convolutional Neural Networks. Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/2847263.2847276"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, Y., Cao, Y., Vrudhula, S., and Seo, J.S. (2017, January 22\u201324). Optimizing Loop Operation and Dataflow in FPGA Acceleration of Deep Convolutional Neural Networks. Proceedings of the 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA \u201917), Monterey, CA, USA.","DOI":"10.1145\/3020078.3021736"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103136","DOI":"10.1016\/j.micpro.2020.103136","article-title":"A fast and scalable architecture to run convolutional neural networks in low density FPGAs","volume":"77","author":"Duarte","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_34","unstructured":"Abdelouahab, K., Pelcat, M., S\u00e9rot, J., and Berry, F. (2018). Accelerating CNN inference on FPGAs: A Survey. CoRR. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wei, G., Hou, Y., Cui, Q., Deng, G., Tao, X., and Yao, Y. (2018, January 16\u201318). YOLO Acceleration using FPGA Architecture. Proceedings of the 2018 IEEE\/CIC International Conference on Communications in China (ICCC), Beijing, China.","DOI":"10.1109\/ICCChina.2018.8641256"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, X., and Liu, B. (2018, January 10\u201314). FCLNN: A Flexible Framework for Fast CNN Prototyping on FPGA with OpenCL and Caffe. Proceedings of the 2018 International Conference on Field-Programmable Technology (FPT), Okinawa, Japan.","DOI":"10.1109\/FPT.2018.00043"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nakahara, H., Yonekawa, H., Fujii, T., and Sato, S. (2018, January 25\u201327). A Lightweight YOLOv2: A Binarized CNN with A Parallel Support Vector Regression for an FPGA. Proceedings of the 2018 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA \u201918), Monterey, CA, USA.","DOI":"10.1145\/3174243.3174266"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Oh, S., You, J.H., and Kim, Y.K. (2020, January 1\u20133). Implementation of Compressed YOLOv3-tiny on FPGA-SoC. Proceedings of the 2020 IEEE International Conference on Consumer Electronics\u2014Asia (ICCE-Asia), Gangneung, Korea.","DOI":"10.1109\/ICCE-Asia49877.2020.9277266"},{"key":"ref_39","unstructured":"Rinc\u00f3n, F., Barba, J., So, H.K.H., Diniz, P., and Caba, J. (2020). A Parameterisable FPGA-Tailored Architecture for YOLOv3-Tiny. Applied Reconfigurable Computing. Architectures, Tools, and Applications, Springer International Publishing."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"75864","DOI":"10.1109\/ACCESS.2021.3081818","article-title":"A Full Featured Configurable Accelerator for Object Detection with YOLO","volume":"9","author":"Pestana","year":"2021","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ahmad, A., Pasha, M.A., and Raza, G.J. (2020, January 10\u201321). Accelerating Tiny-YOLOv3 using FPGA-Based Hardware\/Software Co-Design. Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, Spain.","DOI":"10.1109\/ISCAS45731.2020.9180843"},{"key":"ref_42","unstructured":"Redmond, J. (2021, October 29). Darknet. Available online: https:\/\/github.com\/pjreddie\/darknet."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ayoub, N., and Schneider-Kamp, P. (2021). Real-Time On-Board Deep Learning Fault Detection for Autonomous UAV Inspections. Electronics, 10.","DOI":"10.3390\/electronics10091091"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/11\/280\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:23:41Z","timestamp":1760167421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/11\/280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,30]]},"references-count":43,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["fi13110280"],"URL":"https:\/\/doi.org\/10.3390\/fi13110280","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2021,10,30]]}}}