Jun Jie Chong
Papers
5
Total Citations
49
H-Index
3
About
Jun Jie Chong is a researcher at the forefront of intelligent robotics and smart manufacturing, with a primary focus on deploying deep learning and machine learning on resource-constrained embedded systems. His work directly addresses the critical challenge of achieving high-accuracy computer vision for robotics and autonomous vehicles using low-power devices like the Raspberry Pi. Chong’s major contributions include developing optimized object detection frameworks for smart pick-and-place solutions, as demonstrated in his highly cited 2023 paper (36 citations), which comparatively analyzed cross-validation techniques for lean automation. He has also pioneered the application of zero-shot detection for industrial perception, enabling robots to recognize novel objects like corner castings in shipping containers without prior training. Beyond manufacturing, Chong applies computational methods to biomedical engineering, designing virtual testing platforms for prosthetic knee joints and using machine learning to optimize bio-inspired prosthetic sockets. His work bridges the gap between high-performance AI and practical, edge-deployable systems, making automation more efficient, sustainable, and accessible. With a growing citation record and recent publications extending into 2025, Chong is establishing himself as a key innovator in lean robotics and intelligent perception.
Research Focus
Key Achievements
Top Papers
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