Best ML Model for Real-Time Egg Counting on Raspberry Pi
hello everyone!
I’m working on a real-time egg-counting project using a Raspberry Pi and computer vision. Originally, I thought I could use OpenCV with contour detection for a quick, lightweight solution. The plan was to convert images to grayscale, reduce noise with blurring, and use edge detection to find contours representing eggs. But as I tested it, contour detection turned out to have several big limitations that made it unreliable for this project.
One of the main issues was "lighting sensitivity" even small changes in light, shadows, or reflections would throw off the detection completely, often causing the egg count to vary wildly. Another major problem was "overlapping eggs"—if eggs were close together, contour detection often couldn’t separate them and would count multiple eggs as one. Lastly, "background interference" was a big challenge. The algorithm would sometimes pick up background textures or objects as “eggs,” adding false positives to the count.
Because of these issues, I decided to switch to training a model , which should handle these challenges better. Which specific ML model would you recommend for detecting and counting in real-time on a Raspberry Pi? I need something that can handle varying lighting conditions and overlapping objects while still being lightweight enough to run smoothly on the Pi.
I’m working on a real-time egg-counting project using a Raspberry Pi and computer vision. Originally, I thought I could use OpenCV with contour detection for a quick, lightweight solution. The plan was to convert images to grayscale, reduce noise with blurring, and use edge detection to find contours representing eggs. But as I tested it, contour detection turned out to have several big limitations that made it unreliable for this project.
One of the main issues was "lighting sensitivity" even small changes in light, shadows, or reflections would throw off the detection completely, often causing the egg count to vary wildly. Another major problem was "overlapping eggs"—if eggs were close together, contour detection often couldn’t separate them and would count multiple eggs as one. Lastly, "background interference" was a big challenge. The algorithm would sometimes pick up background textures or objects as “eggs,” adding false positives to the count.
Because of these issues, I decided to switch to training a model , which should handle these challenges better. Which specific ML model would you recommend for detecting and counting in real-time on a Raspberry Pi? I need something that can handle varying lighting conditions and overlapping objects while still being lightweight enough to run smoothly on the Pi.
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