Performance Evaluation of CNN-based Object Detectors on Embedded Devices

Author:

Aminiyeganeh Kasra1ORCID,Coutinho Rodolfo W. L.1ORCID

Affiliation:

1. Concordia University, Montreal, PQ, Canada

Publisher

ACM

Reference28 articles.

1. Rodolfo W. L. Coutinho and Azzedine Boukerche. 2023. Modeling and Performance Evaluation of Collaborative IoT Cross-Camera Video Analytics . In Proc. of the IEEE Int'l Conf. on Communications (ICC). 1--6. Rodolfo W. L. Coutinho and Azzedine Boukerche. 2023. Modeling and Performance Evaluation of Collaborative IoT Cross-Camera Video Analytics. In Proc. of the IEEE Int'l Conf. on Communications (ICC). 1--6.

2. Jifeng Dai et al. 2016. R-FCN: Object Detection via Region-based Fully Convolutional Networks . In Advances in Neural Information Processing Systems , Vol. 29 . Jifeng Dai et al. 2016. R-FCN: Object Detection via Region-based Fully Convolutional Networks. In Advances in Neural Information Processing Systems, Vol. 29.

3. The Pascal Visual Object Classes Challenge: A Retrospective

4. Ross Girshick . 2015 . Fast R-CNN. In Proc. IEEE Int'l Conf. on Computer Vision (ICCV). 1440--1448 . Ross Girshick. 2015. Fast R-CNN. In Proc. IEEE Int'l Conf. on Computer Vision (ICCV). 1440--1448.

5. Jocher Glenn. 2020. Ultralytics/yolov5. https://github.com/ultralytics/yolov5 Accessed on 3/11/2023. Jocher Glenn. 2020. Ultralytics/yolov5. https://github.com/ultralytics/yolov5 Accessed on 3/11/2023.

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