Ponsian M. Robert

About

Ponsian M. Robert is a leading researcher at the intersection of artificial intelligence and sustainable construction, whose work is driving the automation of waste management. His primary research areas include deep learning, computer vision, and robotic waste sorting, with a specific focus on Construction and Demolition Waste (CDW) detection. Robert’s major contribution lies in systematically benchmarking the performance of state-of-the-art deep learning models—comparing single-stage and two-stage detectors—for real-time CDW localisation and classification. His seminal 2023 paper, "Real-time construction demolition waste detection using state-of-the-art deep learning methods," has garnered 95 citations, underscoring its critical role in enabling accurate, high-speed object detection for waste-sorting robots. This work directly addresses the industry’s need for efficient, automated recycling systems, offering a foundational framework for deploying AI in construction waste management. By bridging the gap between algorithmic performance and practical robotic applications, Robert’s research not only advances the field of intelligent waste sorting but also contributes to broader environmental sustainability goals. His findings are essential reading for engineers and researchers developing autonomous systems for circular economy initiatives.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-time construction demolition waste detection using state-of-the-art deep learning methods; single–stage vs two-stage detectors

   95 citations · 2023
2. 2
   Real-Time Construction Demolition Waste Detection Using State-of-The-Art Deep Learning Methods; Single – Stage vs Two-Stage Detectors

   4 citations · 2023

Key Collaborators

Contact & Links