Sewer pipe is an essential infrastructure in the city as it undertakes the transportation and circulation of wastewater resources. But sewer pipe it is easy to have faults and cause serious secondary urban accidents, such as road holes and road collapse. Because of the complex underground circumstance, inspecting large-area sewer pipes using closed-circuit television or periscope television is difficult. In this study, we proposed a collaborative sewer pipe inspection approach by using novel low-cost pipe robotic capsules, which capture the images of the pipeline inner walls when floating with the water flow. A set of workers collaboratively drop and salvage capsules to cover a large-area pipe network. The routes of workers and pipe capsules are optimized by a meta-heuristic algorithm integrating local search and simulated annealing. The deep neural network is used to recognize faults from raw captured images. A field experiment in Shenzhen was conducted to evaluate the performance of the proposed approach. The results demonstrate that it outperforms the naive inspection method with a shorter travel distance and less waiting time. It is also effective for inspecting the large-area sewer pipe networks with an overall precision of 0.92. It will help us to eliminate the potential safety risk of the public and promote the level of urban governance.