Open Access
| Issue |
ITM Web Conf.
Volume 69, 2024
International Conference on Mobility, Artificial Intelligence and Health (MAIH2024)
|
|
|---|---|---|
| Article Number | 04008 | |
| Number of page(s) | 6 | |
| Section | Transactions | |
| DOI | https://doi.org/10.1051/itmconf/20246904008 | |
| Published online | 13 December 2024 | |
- J. Wei et al., “Enhanced object detection with deep convolutional neural networks for advanced driving assistance,” IEEE Trans. on Intelligent Transportation Systems, 2019. [Google Scholar]
- J. Redmon et al., “You only look once: Unified, realtime object detection,” IEEE Conf. on Computer Vision and Pattern Recognition, 2016. [Google Scholar]
- A. Bochkovskiy et al., “Yolov4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020. [Google Scholar]
- O. Rainio et al., “Evaluation metrics and statistical tests for machine learning,” Scientific Reports, 2024, Nature Publishing Group UK London. [Google Scholar]
- P. Franti et al., “Soft precision and recall,” Pattern Recognition Letters, 2023, Elsevier. [Google Scholar]
- C. Mwitta et al., “Evaluation of inference performance of deep learning models for real-time weed detection in an embedded computer,” Sensors, 2024, MDPI. [Google Scholar]
- A. Geiger et al., “Are we ready for autonomous driving? the kitti vision benchmark suite,” IEEE Conf. on Computer Vision and Pattern Recognition, 2012. [Google Scholar]
- S. Groth et al., “Estimating the Execution Time of CNN Inference on GPUs,” MBMV Workshop, 2024. [Google Scholar]
- X. Zhao et al., “A review of convolutional neural networks in computer vision,” Artificial Intelligence Review, 2024, Springer. [Google Scholar]
- F. Z. Guerrouj et al., “Efficient GEMM implementation for vision-based object detection in autonomous driving applications,” Journal of Low Power Electronics and Applications, 2023, MDPI. [Google Scholar]
- R. D. C. Lera et al., “Hardware-efficient convolution algorithms for CNN accelerators: A brief review,” Anais do XX Encontro Nacional de Inteligencia Artificial e Computacional, 2023. [Google Scholar]
- Z. Nazir et al., “Interpretable ML enhanced CNN Performance Analysis of cuBLAS, cuDNN and Ten- sorRT,” ACM/SIGAPP Symposium on Applied Computing, 2023. [Google Scholar]
- J. Zhang et al., “A low-latency FPGA implementation for real-time object detection,” IEEE Int. Symposium on Circuits and Systems, 2021. [Google Scholar]
- H.-H. Nguyen et al., “Towards real-time vehicle detection on edge devices with NVIDIA Jetson TX2,” IEEE Int. Conf. on Cons. Elect., 2020. [Google Scholar]
- A. Anupreetham et al., “High Throughput FPGA- Based Object Detection via Algorithm-Hardware Co-Design,” ACM Trans. on Reconfigurable Technology and Systems, 2024, ACM New York, NY. [Google Scholar]
- J. Zhai et al., “FPGA-based vehicle detection and tracking accelerator,” Sensors, 2023, MDPI. [Google Scholar]
- Y. Wang et al., “Hardware-Software Co-design for Deep Neural Network Acceleration,” Int. Conf. on Service Science, 2023. [CrossRef] [Google Scholar]
- S. Ali et al., “Improved YOLOv4 for Aerial Object Detection,” Signal Processing and Communications Applications Conf., 2021. [Google Scholar]
- O. Byzkrovnyi et al., “Comparison of Object Detection Algorithms for the Task of Person Detection on Jetson TX2 NX Platform,” 2024 IEEE Open Conf. of Electrical, Electronic and Inf. Sciences, 2024. [Google Scholar]
- K. Sarvajcz et al., “AI on the Road: NVIDIA Jetson Nano-Powered Computer Vision-Based System for Real-Time Pedestrian and Priority Sign Detection,” Applied Sciences, 2024, MDPI. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.