Navigation of a lander craft to accurately soft land in a predetermined spot on an extra-terrestrial body is a challenging task. Due to the absence of GPS and real-time ground communication links, the lander craft has to navigate autonomously to the targeted landing site solely with the help of its onboard sensors. One of the major challenges in position estimation of the lander craft is the measurement inaccuracies associated with the inertial navigation systems. Optical landmark detection-based techniques are employed to aid the lander navigation and guidance system. In this technique, craters and their relative positions are used as landmarks for position estimation. Typical crater detection algorithms employ handcrafted feature extractors for crater identification. Recently deep learning-based approaches have been employed for crater detection, mainly for geomorphological studies and cataloguing. This paper proposes using deep learning-based object detection techniques applied to autonomous landing missions and their implementation on a Xilinx MPSoC FPGA device. Prototype hardware has been developed, and the YOLOv4-tiny algorithm has been implemented on it with an inference time of 471 ms per image. The results are presented, and their performance is evaluated.