Sewage pipe defects can significantly affect the urban water environment, like leakage of pollutants through pipe cracks to groundwater. Currently, sewage pipe defects are detected mainly through closed-circuit television inspection, which is conducted manually and is time-consuming. This study proposes an integrated deep-learning-based algorithm to detect and quantify pipe cracks from images, namely the Crack Detection and Characterization (CDC) method. The method is based on models created in two steps (i) crack detection by semantic segmentation, and (ii) crack length quantification using an innovative algorithm. The CDC algorithm is verified by images both artificially created in the laboratory and from actual inspection. For both laboratory and field cases, the CDC method is verified precisely. From the results, the CDC method exhibited a higher accuracy in crack identification and length quantification than other existing models. The results also show that the deblurring process can greatly improve accuracy. This study can contribute to decision-making in sewage pipe maintenance and water environment management by providing an innovative way of more efficient and accurate pipe defect assessment compared with traditional labor-intensive work.

Y₄Si₂O₇N₂: Eu²⁺ phosphor has been prepared by a pretreatment method. Reduction in Eu³⁺ ions into Eu²⁺ by the use of hydrogen iodide (HI) is verified by X-ray absorption near-edge structure (XANES) and electrode potential analysis. Y₄Si₂O₇N₂: Eu²⁺ phosphor has a broad emission band in the range of 400-500 nm. Furthermore, the effect of Zr doping on the structure and luminescence properties of Y₄Si₂O₇N₂: Eu²⁺ phosphor is researched. It found that the Zr doping leads to an emission blueshift, and improves the luminescence intensity and thermal quenching behavior of Y₄Si₂O₇N₂: Eu²⁺ phosphors. Prospectively, the pretreatment approach could be extended to develop other Eu²⁺-doped compounds.