Qi-Chao Mao

Shandong University of Science and Technology

Papers

Total Citations

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About

Qi-Chao Mao is a researcher at the forefront of agricultural robotics and computer vision, with a particular focus on enabling efficient, real-time object detection for autonomous picking systems. His work addresses a critical bottleneck in precision agriculture: the challenge of deploying deep learning-based object detectors on resource-constrained embedded devices. Mao’s most-cited paper, "Fast and Efficient Non-Contact Ball Detector for Picking Robots" (2019, 14 citations), tackles the high computation overhead and power consumption that plague traditional deep learning models on on-board hardware. By developing a lightweight, non-contact detection framework, he has contributed to making picking robots faster, more energy-efficient, and practically deployable in the field. This work is foundational for researchers and engineers seeking to bridge the gap between high-performance AI and real-world robotic applications. Mao’s research is characterized by a pragmatic focus on computational efficiency without sacrificing detection accuracy, a balance that is essential for the next generation of autonomous agricultural machinery. His contributions are steadily gaining recognition as the demand for intelligent, low-power robotic solutions continues to grow.