Jing Zheng

Papers

Total Citations

H-Index

About

Jing Zheng is a researcher specializing in computer vision, image processing, and intelligent pipeline inspection systems. Their work sits at the intersection of deep learning-based image enhancement and practical industrial robotics applications, with a particular focus on improving automated detection capabilities in challenging real-world environments. Zheng's most recognized contribution addresses a critical problem in underground infrastructure maintenance: the degradation of visual data captured by pipeline inspection robots operating in dark, foggy, and haze-filled drainage environments. By developing a pipeline image haze removal system leveraging the dark channel prior algorithm and deploying it on a cloud processing platform, Zheng demonstrated an innovative approach to enhancing image clarity under conditions where traditional detection methods routinely fail. This work, which has garnered 5 citations since its 2020 publication, reflects a broader commitment to bridging theoretical image processing techniques with the demanding operational needs of pipeline fault detection systems. Zheng's research contributes meaningfully to the safety and longevity of underground drainage infrastructure, offering engineers and municipalities more reliable tools for identifying structural defects before they escalate into costly failures. Their work remains a valuable reference for researchers developing vision-based robotic inspection technologies.