Mobile QR Code QR CODE

REFERENCES

1 
Choi J., Elezi I., Lee H-J., Farabet C., Alvarez J. M., Oct. 2021, Active Learning for Deep Object Detection via Probabilistic Modeling, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 10264-10273.URL
2 
Ravindran R., et al. , Mar. 2021, Multi-Object Detection and Tracking, Based on DNN, for Autonomous Vehicles: A Review, in IEEE Sens. J., Vol. 21, No. 5, pp. 5668-5677DOI
3 
Zhao X., et al. , May 2020, Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications, in IEEE Sens. J., Vol. 20, No. 9, pp. 4901-4913DOI
4 
Choi J., Chun D., Kim H., Lee H-J., Oct. 2019, Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV)URL
5 
Womg A., Shafiee M. J., Li F., Chwyl B., May 2018, Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection, in Proc. 15th Conf. on Comput. Robot Vision (CRV), pp. 95-101.DOI
6 
Redmon J., Farhadi A., 2018., YOLOv3: An incremental improvement, arXiv preprint, arXiv:1804.02767DOI
7 
Nguyen D. T., Nguyen T. N., Kim H., H.-J Lee. , 2019, A High-Throughput and Power-Efficient FPGA Implementation of YOLO CNN for Object Detection, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., Vol. 27, No. 8, pp. 1861-1873DOI
8 
Nguyen D. T., Hung N. H., Kim H., Lee H. J., 2020, An Approximate Memory Architecture for Energy Saving in Deep Learning Applications, IEEE Trans. Circuits Syst. I, Reg. Papers, Vol. 67, No. 5, pp. 1588-1601DOI
9 
Nguyen D. T., Kim H., Lee H. J., Chang I. J., May. 2018, An approximate memory architecture for a reduction of refresh power consumption in deep learning applications, in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), pp. 1-5DOI
10 
Kang D., Kang D., Kang J., Yoo S., Ha S., Mar. 2018, Joint optimization of speed, accuracy, and energy for embedded image recognition systems, in Proc. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 715-720DOI
11 
Sandler M., Howard A., Zhu M., Zhmoginov A., Chen L., Jun. 2018, MobileNetV2: Inverted Residuals and Linear Bottlenecks, in Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510-4520URL
12 
Nguyen X. T., Nguyen T. N., H.-J Lee , Kim H., Dec. 2020, An Accurate Weight Binarization Scheme for CNN Object Detectors with Two Scaling Factors, IEIE Transactions on Smart Processing & Computing, Vol. 9, No. 6, pp. 497-503DOI
13 
Choi J., Chun D., Lee H-J., Kim H., Aug. 2020, Uncertainty-based Object Detector for Autonomous Driving Embedded Platforms, in Proc. IEEE Int. Conf. Artifici. Intell. Circuits Syst. (AICAS), pp. 16-20DOI
14 
Zhang Y., Shen Y., Zhang J., Apr. 2019, An improved tiny-yolov3 pedestrian detection algorithm, Int. J. Light Electron Opt., Vol. 183, pp. 17-23DOI
15 
Xiao D., et al. , Jul. 2019., A target detection model based on improved tiny-yolov3 under the environment of mining truck, IEEE Access, Vol. 7DOI
16 
Zhao Q., et al. , Jan. 2019, M2Det: A single-shot object detector based on multi-level feature pyramid network, in Proc. AAAI Conf. Artif. Intell. (AAAI), pp. 9259-9266DOI
17 
SEKONIX Corp. , Feb. 2020., SF332X-10X Family Preliminary Datasheet, [Online]. Available: http://sekolab.com/products/camera/URL
18 
H. Zhang , et al. , Apr. 2018, mixup: Beyond empirical risk minimization, in Proc. Int. Conf. Learn. Represent. (ICLR), pp. 1-13Google Search
19 
Takahashi R., Matsubara T., Uehara K., 2020, Data augmentation using random image cropping and patching for deep CNNs, IEEE Trans.Circuits Syst. Video Technol., Vol. 30, No. 9, pp. 2917-2931DOI
20 
Krizhevsky A., Sutskever I., Hinton G. E., Dec. 2012, ImageNet classification with deep convolutional neural networks, in Proc. Adv. Neural Inf. Process. Syst., pp. 1097-1105Google Search
21 
He K., Zhang X., Ren S., Sun J., Jun. 2016, Deep residual learning for image recognition, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 770-778URL
22 
Hemmati M., B-Abhari M., Niar S., 2019, Adaptive Vehicle Detection for Real-time Autonomous Driving System, in Proc. Des. Autom. And Test in Eur.Conf. & Exhib.DOI
23 
Yu F., et al. , Jun. 2020, BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)URL
24 
Geiger A., Lenz P., Urtasun R., Jun. 2012, Are we ready for autonomous driving? the kitti vision benchmark suite, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 3354-3361DOI
25 
NVIDIA Corp. , Dec. 17, 2018, NVIDIA Xavier DocumentationGoogle Search