April 23, 2026
Duck, donuts, and phone-shot drama
My phone replaced a brass plug
Phone beats brass plug at target scoring — shooters cheer, skeptics shrug
TLDR: A coder built a phone-based tool to auto-score bullet holes, replacing the old brass plug at the range. Commenters split between delight and “why bother,” debated how scoring works, and one even said it might lure them back to shooting — a quirky win for fun, useful tech.
The internet did a double take at a Scottish shooting range where an iOS engineer turned his phone into a score judge, replacing the old brass plug used to measure bullet holes. Commenters were equal parts charmed and confused. One lapsed competitor, owning his bad ranking, said this project "might get me back to the range" — instant nostalgia bait for every dusty range bag. Another voice shot back with peak pragmatism: why automate ring counting when “any human can do it?” That set off the classic overengineering vs. joy-of-tinkering debate. Meanwhile, purists got hung up on rules: one asked if scoring uses the outer ring instead of the center — cue a mini explainer that different disciplines judge by whether the hole touches a higher-value ring.
Fans swooned over the vibe: sausage rolls from Greggs, DUCK signs, head bumps, doughnuts — and then, bam, a phone app that finds holes the way a bar code scanner finds prices. The creator admits the trickiness: a bullet hole is a missing thing, which stumps normal image tools, so he leaned on research promising near-perfect detection under strict conditions. The community? Half “This… is beautiful,” half “Cool, but… why?” and 100% entertained by a journey that runs from homemade charcuterie to high-precision targets via smartphone wizardry.
Key Points
- •Manual scoring at a shooting range near Edinburgh uses brass plug gauges to determine ring-touching hits, which the author found tedious and error-prone.
- •Initial automation using Apple’s Vision framework failed to reliably detect bullet holes because holes are negative space and the model misidentified target features.
- •Image preprocessing attempts (grayscale, inversion, noise adjustments) did not resolve edge cases such as shots landing on ring lines.
- •A geometry-based approach—detecting the target’s ring structure first, then identifying holes—was proposed as more robust.
- •A 2012 paper by Rudzinski and Luckner (Warsaw University of Technology) reported ~99% hole detection but required constraints like flat ISSF targets and low camera angles.