Mingqiang Wang

Jiangsu University of Science and Technology

Papers

Total Citations

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About

Mingqiang Wang is a researcher at the forefront of applying artificial intelligence to construction and demolition waste management. His primary research areas include deep learning, computer vision, and intelligent waste sorting systems. Wang’s most notable contribution is the development of an improved YOLO-V7 network for the real-time detection of impurities in construction and demolition waste, a breakthrough that significantly enhances the efficiency and accuracy of automated waste processing. His work addresses a critical environmental challenge by enabling rapid, on-site identification of contaminants, thereby improving recycling rates and reducing landfill burden. With his 2024 paper already garnering 11 citations, Wang’s research is gaining traction for its practical impact on sustainable construction practices. His innovative approach combines state-of-the-art object detection algorithms with real-world industrial applications, positioning him as a rising expert in AI-driven environmental solutions. Wang’s achievements demonstrate a powerful synergy between cutting-edge technology and pressing ecological needs, making his work essential reading for students and researchers interested in smart waste management and green engineering.