Pigeons may look harmless, but for people dealing with repeated nesting on balconies, they can quickly become a serious nuisance. Feathers, droppings, nesting material, noise, and constant cleaning can turn a peaceful balcony into a daily maintenance problem.
One maker, Maximilian Bachl, decided to approach the problem like an engineering challenge. Instead of manually chasing pigeons away, he built an AI-powered automated pigeon defense system that can detect a bird, aim at it, and spray water automatically.
The project combines computer vision, robotics, embedded computing, and a modified water gun into a surprisingly sophisticated automated system.
From a Pigeon Problem to an AI Solution
The basic idea behind the project is simple: if a camera can identify when a pigeon enters the balcony, a small robotic system can respond automatically.
The system works through four main stages.
First, a USB camera continuously captures live video of the area being monitored. The video is then processed by an AI vision model running on an Orange Pi 5.
When the system recognizes a pigeon, it determines where the bird is located within the camera's field of view. That information is then used to control two servo motors.
The servos rotate the water gun toward the detected target.
Finally, an electronic trigger activates the modified battery-powered water gun, spraying water in the pigeon's direction.
The entire process happens automatically, without someone needing to watch the camera or operate the water gun manually.
The Brain: Orange Pi 5 and AI Vision
At the center of the project is an Orange Pi 5, a compact single-board computer built around the Rockchip RK3588 processor.
The RK3588 is particularly interesting for AI and computer-vision applications because it includes a dedicated neural processing unit (NPU). Instead of relying entirely on the CPU for AI calculations, compatible machine-learning workloads can take advantage of this specialized hardware.
For this project, the detection system uses YOLO-World v2, an open-vocabulary computer-vision model.
Traditional object-detection systems generally need to be trained to recognize a fixed list of objects. For example, a model might be trained specifically to recognize people, cars, dogs, or birds.
YOLO-World takes a more flexible approach.
Its open-vocabulary capabilities allow the system to search for objects based on text descriptions rather than being restricted to a small, predetermined list of categories.
That makes the robotic system considerably more versatile than a simple "pigeon detector."
How the Automated Targeting Works
Detecting a pigeon is only half the challenge.
The system also needs to point the water gun in approximately the right direction.
That's where the two servo motors come in.
One servo can control movement along one axis, while the second servo controls movement along another axis. Together, they create a small pan-and-tilt mechanism capable of directing the water gun toward different positions.
When the AI identifies a pigeon, its location in the camera image provides the information needed for targeting.
The software translates the detected position into movement commands for the servos.
The mechanism then rotates toward the detected location.
Once the targeting system is positioned, the electronics can activate the water gun.
To accomplish this, the project uses resistors and a transistor as part of the electronic trigger mechanism. These components allow the Orange Pi to control the water gun's electrical activation without directly connecting the computer's control circuitry to the higher-current motor system.
This is an important part of the design because computers such as the Orange Pi are not designed to directly power motors or other high-current devices.
A Surprisingly Simple Hardware Setup
Despite the sophisticated AI involved, the hardware required for the system is relatively compact.
The reported setup includes:
Orange Pi 5 – the main computing platform
Rockchip RK3588 NPU – used to accelerate compatible AI workloads
USB camera – captures live video
YOLO-World v2 – performs open-vocabulary object detection
Two servo motors – control the aiming mechanism
Modified battery-powered electric water gun – provides the deterrent
Resistors and transistor – interface with the water gun's trigger circuit
Together, these components transform an ordinary balcony into a small automated computer-vision experiment.
Why Water Instead of Something More Aggressive?
The project's deterrence method is particularly notable.
Rather than physically attacking the birds, the system uses water as a non-lethal deterrent. The goal is simply to make the location uncomfortable enough that the pigeon leaves.
That makes the concept closer to an automated wildlife-deterrence system than a conventional robotic weapon.
However, any automated system that moves or sprays objects should be designed carefully. It needs appropriate limits so that it does not accidentally spray people, neighboring balconies, pets, or other unintended targets.
The physical environment also matters. Water and electronics are a particularly important combination, so weatherproofing, electrical isolation, and safe placement would be essential for any real-world deployment.
The Wildest Part: It Doesn't Have to Detect Pigeons
Perhaps the most interesting aspect of the project isn't actually the water gun.
It's the open-vocabulary AI model behind it.
Because YOLO-World can work with text-based object concepts, the system isn't fundamentally restricted to pigeons.
In principle, the detection target could be changed to other objects or animals that the model can reliably recognize.
For example, the same concept could potentially be adapted to detect squirrels, cats, raccoons, or other unwanted visitors.
That means the underlying technology is much broader than a pigeon-control device.
The project demonstrates a general pattern:
Camera → AI detection → target location → robotic movement → automated response.
The same architecture could potentially be adapted for many legitimate automation and monitoring applications.
A Glimpse Into the Future of Small Robots
What makes Maximilian Bachl's project fascinating is how many technologies have become accessible enough to combine in a relatively small DIY system.
A few years ago, building something that could continuously watch an environment, understand what it was seeing, determine where an object was located, move a mechanism toward it, and trigger an action would have required considerably more specialized equipment.
Today, a compact single-board computer, inexpensive camera, servo motors, and an AI model can accomplish the basic concept.
The project also demonstrates an important shift in robotics.
Robots don't necessarily need to follow rigid instructions anymore. With modern computer vision, they can increasingly perceive their surroundings and make decisions based on what they see.
In this case, the decision is straightforward: detect a pigeon and activate a harmless deterrent.
But the same technological pattern can be applied to much more useful applications, including automated inspection, agriculture, wildlife monitoring, security observation, and smart-home automation.
From Balcony Annoyance to Robotics Experiment
What began as a simple problem—pigeons repeatedly nesting on a balcony—ultimately became an impressive demonstration of AI-powered robotics.
The system combines computer vision, edge AI, robotic control, electronics, and automated decision-making into one compact setup.
Its biggest lesson isn't really about pigeons.
It's about how easily modern AI can be connected to the physical world.
A camera can now give a computer "eyes." An AI model can interpret what those eyes see. Servo motors can provide movement, and simple electronics can trigger a physical response.
Put all of those pieces together, and even a balcony can become a testing ground for autonomous robotics.
And that's perhaps the most fascinating part: the technology doesn't just recognize the world—it can react to it.
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