Before revision:  With the color images transformed from bitstreams as the training dataset , a deep learning-based object detection algorithm is applied for generating the function block detection results. The effects of EDA tools, input size of the deep neural network , and the data arrangement of representation on the detection accuracy are explored.
After revision:  With the color images transformed from bitstreams as the training dataset , a deep learning-based object detection algorithm is applied to this transformed image, and the objects thus detected can be reflected back to determine the function blocks of the original FPGA design. The deep neural network , and the data arrangement of representation on the detection accuracy are explored.