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As an important municipal infrastructure that ensures the safety of urban drainage, urban underground pipe network systems commonly suffer from many disease problems during long-term overload operation. Traditional detection technology CCTV relies on the professional skills and prior experience of professionals. Therefore, in order to realize automated urban underground pipe network defects and diseases, an urban pipe network defect and disease detection algorithm was proposed and successfully used in actual projects. The adaptive CA attention mechanism is adopted to effectively weaken the negative impact of complex backgrounds; the key method of decoupling defect classification and regression enables the detection part to make full use of defect texture and edge information, thereby improving the accuracy of small-sized defects; The application of SIoU loss function introduces angle term trade-offs into the algorithm, effectively speeding up the convergence. After experimental testing, an average accuracy of 71.1% was achieved, which was 5.3% higher than the original algorithm and satisfied the practical engineering application.