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In the context of various application scenarios and/or for the sake of strengthening field-programmable gate array (FPGA) security, the system functions of an FPGA design need to be analyzed, which can be achieved by systematically partitioning the FPGA's bitstream into manageable functional blocks and detecting their functionalities thereafter. In this paper, we propose a novel deep learning-based FPGA function block detection method with three major steps. In specific, we first analyze the format of the bitstream to obtain the mapping relationship between the configuration bits and configurable logic blocks because of the discontinuity of the configuration bits in the bitstream for one element. In order to reap the maturity of object detection techniques based on deep learning, our next step is to convert an FPGA bitstream to an image, following the proposed transformation method that takes account of both the adjacency nature of the programmable logic and the high degree of redundancy of configuration information. Once the image is obtained, a deep learning-based object detection algorithm is applied to this transformed image, and the objects detected can be reflected back to determine the function blocks of the original FPGA design. The deep neural network used for function block detection is trained and validated with a specially crafted bitstream/image dataset. Experiments have confirmed high detection accuracy of the proposed function detection method, showing a 98.11% of mean Average Precision (IoU=0.5) for 10 function blocks within a YOLOv3 detector implemented on Xilinx Zynq-7000 SoCs and Zynq UltraScale+ MPSoCs.