Abstract
In this paper we propose different FPGA implementations of a Convolutional Neural Network where the use case is to detect road cracks via images. The work is based on the network proposed by the current state of the art in the field. We deploy the network using the MATLAB Deep Learning HDL toolbox on all available AMD-Xilinx platforms, including different data types. In particular, we use the ZC706 and ZCU102 development boards. In order to infer the CNN, we apply a single precision and an 8-bit integer data type quantization. The implementation results show that the detection accuracy of 99.6% is the same of the state of the art, even though the network is quantized. We also obtain a speed-up of the CNN reaching up to 313.2 Frames Per Second while requiring only 45.85 mJ to process one frame. The proposed implementations are therefore a viable solution for a fast and low-power crack detection system.
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Acknowledgements
The authors would like to thank Advanced Micro Devices, Inc. (AMD) for providing the FPGA hardware and software tools with the Xilinx University Program.
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Canese, L., Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Re, M., Spanò, S. (2023). FPGA-Based Road Crack Detection Using Deep Learning. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_7
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