Most of the existing domain adaptive visual object detection algorithms are based on two-stage detector design and fail to exploit the semantic topological relationship between different elements in the image space， resulting in suboptimal cross-domain adaptation performance. Therefore， in this paper a domain adaptive visual object detection algorithm based on multi-granularity relationship reasoning is proposed. Firstly， a coarse-grained patch relationship reasoning module is proposed， which uses the coarse-grained patch graph structure to capture the topological relationship between the foreground and background and perform cross-domain adaptation on the foreground area. Then， a fine-grained semantic relationship reasoning module is designed to reason about the fine-grained semantic graph structure to enhance cross-domain multi-category semantic dependencies. Finally， a granularity-induced feature alignment module is proposed to adjust the weight of feature alignment according to the affinity of the nodes， thereby improving the adaptability of the detection model when facing overall scene changes. The experimental results on multiple cross-domain scenarios of autonomous driving verify the robustness and real-time performance of the proposed algorithm.
