Improved estimation for just-noticeable visual distortion
Signal Processing
Automated diagnosis of sewer pipe defects based on machine learning approaches
Expert Systems with Applications: An International Journal
Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis
Expert Systems with Applications: An International Journal
A robust approach for automatic detection and segmentation of cracks in underground pipeline images
Image and Vision Computing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
| Hi-index | 12.05 |
Closed circuit television (CCTV) has been applied in many developing or developed counties for sewer inspection due to its low setup cost and technical requirement. Several automated diagnosis systems of sewer pipe defects had been developed to assist the technicians in interpreting or classifying sewer pipe defects. However, many researchers pointed out that good image quality is the prerequisite for accurate interpretation and diagnosis of CCTV inspection but has not a proper evaluation approach. In this paper, a CCTV image quality index considering both of the luminance distortion and the contrast distortion of a CCTV image compared by reference images is proposed and was applied to assess the image quality of the CCTV images shot for a sewer house-connection project. The experimental result indicates that rather than luminance contrast plays a more important role in the CCTV image quality that can be effectively improved by contrast enhancement. Since CCTV image quality can hardly distinguished by human eyes, the proposed image quality index can provide helpful information to efficiently assist the on-site technicians in precisely shooting better CCTV images for the pipe defection. Additionally, a sensitivity analysis of contrast stretch was implemented to quantify the CCTV image quality improvement. CCTV imaging conditions, such as pipe materials and imager status, are found as the factors affecting the CCTV image quality. In the future, a real-time CCTV image quality assessment will be developed by modifying the CCTV image quality index as an instantaneous reference for imaging adjustment that can be expected to be practicable for the on-site sewer inspection because of the extremely short computation time.