Software-defined network (SDN) is a new network architecture that separates the control and forwarding planes. It can schedule and optimize network resources based on global information. Nevertheless, precise scheduling requires accurate measurement of information on the entire network (including the status of all switching devices in the network and all link information in the topology). In-band network telemetry (INT) can realize the collection of relevant information while forwarding data packets, and configuration of detection paths which cover the entire network is one of the key issues to be solved for INT. However, existing detection path configuration methods for INT have the following problems. (1) The deployment of a large number of detection nodes is required in advance, which leads to increased maintenance overhead. (2) The detection path is too long, which results in the length of detection packet exceeding the MTU value in the network. (3) The redundant detection paths cause the traffic load introduced by the measurement to account for too much of the overall network traffic. (4) The recovery time of the detection path adjustment under the dynamically changing topology is too long. In order to solve the above problems, an adaptive detection path configuration method for in-band network telemetry in SDN based on graph segmentation (ACGS) is proposed. The basic idea is to divide the network topology with the graph segmentation to restrict the length of detection path by controlling the topology scale, solve the Euler circuit in the divided subgraph to obtain a detection path that only traverses the directed edges in the subgraph once, to avoid the problems of too many detection nodes and high detection path redundancy; and use the combination of local adjustment and global adjustment to solve the problem of long recovery time of the detection path when the topology changes dynamically. The experimental results prove that the ACGS method can realize the INT detection path configuration in SDN with moderate detection path length, fewer detection nodes, lower detection path redundancy, and faster adjustment under the dynamically changing topology.