Mateus Coelho

Papers

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About

Mateus Coelho is a researcher at the forefront of computer vision and edge computing, with a focused expertise in real-time object detection and its deployment on resource-constrained hardware. His most cited work, "Real-time Object Detection Performance Analysis Using YOLOv7 on Edge Devices" (2024, 14 citations), provides a critical benchmark for running state-of-the-art detection models on platforms like NVIDIA Jetson and Raspberry Pi. Coelho’s major contribution lies in systematically evaluating trade-offs between accuracy, latency, and power consumption, enabling practical applications in security systems, autonomous vehicles, and robotics. By quantifying how YOLOv7 performs on edge devices, his research bridges the gap between high-performance AI algorithms and real-world deployment constraints. This work is essential for engineers and researchers seeking to implement efficient, low-latency vision systems without relying on cloud infrastructure. Coelho’s findings have already influenced the design of embedded AI solutions, making him a key voice in the growing field of edge intelligence. His ongoing efforts continue to push the boundaries of what is possible in real-time visual perception on limited hardware.