Abstract
Object detection is the task of detecting the position of objects in an image or video as well as their corresponding class. The current state of the art approach that achieves the highest performance (i.e. fps) without significant penalty in accuracy of detection is the YOLO framework, and more specifically its latest version YOLOv3. When embedded systems are targeted for deployment, YOLOv3-tiny, a lightweight version of YOLOv3, is usually adopted. The presented work is the first to implement a parameterised FPGA-tailored architecture specifically for YOLOv3-tiny. The architecture is optimised for latency-sensitive applications, and is able to be deployed in low-end devices with stringent resource constraints. Experiments demonstrate that when a low-end FPGA device is targeted, the proposed architecture achieves a 290x improvement in latency, compared to the hard core processor of the device, achieving at the same time a reduction in mAP of 2.5 pp (30.9% vs 33.4%) compared to the original model. The presented work opens the way for low-latency object detection on low-end FPGA devices.
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References
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
Girshick, R.: Fast R-CNN. In: The IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448, December 2015
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580–587, June 2014
Lin, T., et al.: Microsoft COCO: common objects in context. CoRR abs/1405.0312 (2014). http://arxiv.org/abs/1405.0312
Liu, B., Xu, X.: FCLNN: a flexible framework for fast CNN prototyping on FPGA with OpenCL and Caffe. In: 2018 International Conference on Field-Programmable Technology (FPT), pp. 238–241, December 2018
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
LogicTronix: Yolov3 tiny tutorial: Darknet to caffe to xilinx dnndk (2019). https://logictronix.com/wp-content/uploads/2019/08/Yolov3-Tiny-Tutorial-Darknet-to-Caffe-Conversion-and-Implementation-on-Xilinx-DNNDK_August12_2019.pdf
Nakahara, H., Yonekawa, H., Fujii, T., Sato, S.: A lightweight YOLOv2: a binarized CNN with a parallel support vector regression for an FPGA. In: 2018 ACM/SIGDA International Symposium, pp. 31–40, February 2018
Nguyen, D.T., Nguyen, T.N., Kim, H.: A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 27(8), 1861–1873 (2019)
Nvidia: Geforce gtx titan x user guide (2014). https://www.nvidia.com/content/geforce-gtx/GTX_TITAN_X_User_Guide.pdf
Preußer, T.B., Gambardella, G., Fraser, N., Blott, M.: Inference of quantized neural networks on heterogeneous all-programmable devices. In: 2018 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 833–838, March 2018
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, June 2016
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement, April 2018. https://pjreddie.com/media/files/papers/YOLOv3.pdf
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2015)
Venieris, S.I., Kouris, A., Bouganis, C.S.: Toolflows for mapping convolutional neural networks on FPGAs: a survey and future directions. ACM Comput. Surv. 51(3), 56:1–56:39 (2018). https://doi.org/10.1145/3186332. http://doi.acm.org/10.1145/3186332
Wai, Y.J., bin Mohd Yussof, Z., bin Salim, S.I., Chuan, L.K.: Fixed point implementation of Tiny-Yolo-v2 using OpenCL on FPGA. Int. J. Adv. Comput. Sci. Appl. 9(10), 506–512 (2018)
Wei, G., Hou, Y., Cui, Q., Deng, G., Tao, X., Yao, Y.: YOLO accelration using FPGA architecture. In: 2018 IEEE/CIC International Conference on Communications in China (ICCC), pp. 734–735, August 2018
Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212–3232 (2019)
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Yu, Z., Bouganis, CS. (2020). A Parameterisable FPGA-Tailored Architecture for YOLOv3-Tiny. In: Rincón, F., Barba, J., So, H., Diniz, P., Caba, J. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science(), vol 12083. Springer, Cham. https://doi.org/10.1007/978-3-030-44534-8_25
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