Resolution Enhancement for Thermographic Inspection in Industrial Plant Using Deep Convolutional Networks

As the importance of maintainability for the energy efficiency of industrial plants is emphasized, thermographic inspection has been widely used as a key technique. However, the thermographic inspection cannot provide clear information due to the limitation of low resolution in inspecting wide areas where the worker cannot acquire data in close, such as hazardous environments and the ceiling with pipelines. Therefore, this paper proposes a resolution enhancement method for thermographic inspection in industrial plants based on deep convolutional network. The proposed network involves patch extraction and representation, non-linear mapping, and reconstruction. The proposed method was validated with 210 thermal images obtained from real cogeneration plants. Experimental results show that the method can provide the clear boundaries of instruments with an average PSNR of 32.51, which is superior than the bicubic interpolation of 29.92. The proposed method can be implemented in energy efficiency monitoring and automatic defect detection for thermal inspection in industrial plants by helping to detect defects that can be unchecked in low resolution images.