The introduction of unmanned autonomous systems to realize intelligent inspection of canal networks is of great significance to the construction, monitoring and maintenance of water conservancy projects. When unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGV) are used to cooperate in the inspection of canals, UAVs carry out patrol inspection work over the canals, and unmanned vehicles can be used as UAV carrier platforms and energy supply stations, which is helpful to realize rapid autonomous inspection of large-scale canal networks. However, the dual constraints of canal network and road network bring great difficulty to the path planning of unmanned systems. In view of the above problems, aiming to minimize the time to complete the entire inspection task. Firstly, based on the degree constraint, a canal network segmentation method was proposed to allocate the inspection task to the UAVs, so that the UAV did not need to take off or land to recharge when inspecting each canal segment. Then the optimal movement path for UAVs and UGV was calculated based on genetic algorithm. Finally, through the real-world example verification, when the UAVs were operating at a constant speed of 60km/h and the UGV was operating at a speed of 40km/h, the inspecting speed of the unmanned system was 8.4~9.8 times that of the human inspection based on the regular speed of 2km/h.