Clever & Ember
Clever
Hey Ember, I've been thinking about building a low‑cost, AI‑powered perimeter guard system for our community. Want to brainstorm how we could make it fast, reliable, and keep everyone safe?
Ember
Sounds like a solid plan, and I’m all in. Let’s keep it simple and fast: use a Raspberry Pi cluster with cheap Pi cameras for visual feed, plug in a YOLO‑tiny model for object detection—fast and cheap. Add IR motion sensors on the perimeter for a low‑power trigger, so the Pi only turns on when something moves. For communication, a local Wi‑Fi mesh so alerts ping the whole block instantly. If we budget a bit more, throw in a couple of low‑cost drones to patrol gaps, but keep the ground system simple. Trust the community to flag any false alarms and we’re golden. What’s our first step?
Clever
Great, let’s start by laying out a quick architecture diagram in our heads. First, sketch the perimeter map and mark all the “hard‑to‑see” spots—those will be our camera spots. Then pick the exact Pi model and camera specs that fit your budget and range needs. After that, we’ll decide the exact YOLO‑tiny config and training data tweaks. Once that’s nailed, wire up the IR sensors and write a tiny daemon to wake the Pi when motion is detected. Finally, spin up a basic mesh node on each block corner to relay alerts. Sound doable?
Ember
That map is right in the sweet spot—hard‑to‑see spots turned into our eyes. Pick a Pi Zero 2W for budget, add a 5MP camera that sees 120 degrees, and we’re good for most corners. YOLO‑tiny with a 416‑pixel width will keep latency under a second if we trim the classes to just people, animals, and intruders. The IR sensor daemon can be a quick Python loop that powers the Pi on pulse; we’ll add a watchdog to keep it sleeping when the sky is clear. Mesh nodes on each block corner—maybe just a couple of ESP32s running a lightweight broker, and the Pi pushes to that when an alert comes in. It’s tight, but I can see the whole block lit up in real time. Let’s get the sketches out, grab a few Pi Zero 2Ws, and start pulling the pieces together. Onward!
Clever
Sounds solid—grab the Pi Zeros, set up the 5MP cameras, and let’s write a quick YOLO‑tiny inference script first. Once we confirm the detection speed, we can hook up the IR wake‑up loop and test the ESP32 mesh. Keep the code lean, so the whole block stays responsive. Ready to dive in?
Ember
Got it. Start by pulling a tiny YOLO‑tiny from Ultralytics, strip it down to just a few classes, and run it on the Pi Zero with OpenCV and the edge‑TPU if we have one. Make the loop a single threaded thread that grabs frames, feeds them, and spits out a confidence threshold. Once we hit <200 ms per frame, we’re good to hook up the IR sensor to wake the Pi and spin the ESP32 mesh. Let’s keep the code lean, no extra bells—just detection and a quick send. On it.
Clever
Sounds like a plan—let’s pull the Ultralytics repo, prune the class list to people, animals, intruders, and set the input to 416x416. I’ll start a test on a Pi Zero 2W, benchmark the frame time, and tweak the batch size to keep it under 200 ms. Once we hit that target, we’ll wire up the IR wake‑up and spin up the ESP32 broker. On to the code!