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After months of hard work and effort, I have finally completed and published my new proof-of-concept research project on developing an AI-driven plastic surface anomaly detection mechanism by the application of direct UV (ultraviolet) radiation.
⛓️ ⚙️ 🔦 🟣 Development Process
#️⃣ I utilized three different UV light (radiation) sources, providing varying wavelength ranges.
✅ 275 nm
✅ 365 nm
✅ 395 nm
#️⃣ I designed three cubes showcasing different surface defect stages.
✅ none
✅ high
✅ extreme
#️⃣ I printed these three cubes with five different plastic materials (filaments) to increase my sample size.
✅ Matte White
✅ Matte Khaki
✅ Shiny (Silk) White
✅ UV-reactive White (Fluorescent Blue)
✅ UV-reactive White (Fluorescent Green)
#️⃣ I applied two different types of external camera filters, making it four different filter options.
✅ UV bandpass filter (glass)
✅ Gel filters with low light transmission
✅ Gel filters with medium light transmission
✅ Gel filters with high light transmission
I built my FOMO-AD (visual anomaly detection model) with @EdgeImpulse. Then, I capitalized on the Raspberry Pi 5’s dual-CSI ports, which allowed me to utilize two different types of camera modules (regular Wide and NoIR Wide) simultaneously.
I developed a sprocket-chain circular conveyor mechanism, which uses neodymium magnets and two magnetic Hall-effect sensors to align plastic object surfaces and the focal points of the camera modules.
Then, I decided to develop a unique controller board (PCB) as the primary interface of the circular conveyor. To reduce the footprint of the PCB, I decided to utilize an ATmega328P and design the controller board (4-layer @Elecrow1 PCB) as a custom @Raspberry_Pi 5 shield (hat).
Finally, since I wanted to simulate the experience of operating an industrial-grade automation system, I developed an authentic web dashboard for the circular conveyor, which lets the user:
✅ review real-time inference results with timestamps,
✅ sort the inference results by camera type (regular or NoIR),
✅ and enable the @twilio integration to get the latest surface anomaly detection notifications as SMS.
➡️ By referring to the following @Hacksterio tutorial, you can inspect the in-depth feature, design, and code explanations with the challenges I faced during the overall development process.
hackster.io/kutluhan-aktar…
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