Jinxin Tan

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About

Jinxin Tan is a leading researcher at the intersection of artificial intelligence and environmental engineering, with a primary focus on deep learning applications for urban water infrastructure. His most impactful work, "Deep learning-assisted automated sewage pipe defect detection for urban water environment management" (2023), has garnered 40 citations and represents a significant breakthrough in smart city water management. Tan's major contribution lies in developing automated computer vision systems that can rapidly and accurately identify defects in sewage pipes—such as cracks, blockages, and corrosion—using deep convolutional neural networks. This innovation dramatically reduces the need for manual inspection, lowering costs and improving the reliability of urban drainage networks. By integrating AI with environmental monitoring, Tan's research directly addresses critical challenges in water pollution control and infrastructure maintenance, offering scalable solutions for cities worldwide. His work has been recognized for its practical impact, bridging the gap between cutting-edge machine learning and real-world environmental management. Tan continues to advance the field by exploring novel architectures for defect classification and segmentation, positioning him as a key contributor to the growing domain of AI-driven environmental sustainability.