EO/IR cameras, gimbals, sensor fusion, and laser targeting systems
When you spot a drone in the sky, what really matters isn't the airframe — it's the equipment it's carrying. What actually makes a drone "smart" isn't its body, but the camera and sensor package mounted on it. This package is called the "payload," and it works as the drone's eyes, ears, and in a sense, its brain. In this post, we'll explain in plain language what these systems are, how they work, and how they come together during a real operation.
The drone itself is really just a carrier — a platform that stays airborne. The actual work happens in the equipment mounted on it. This equipment package usually consists of several different cameras, sometimes a laser system, and a moving housing that protects them all.
The shared goal of these systems is this: to see what's happening beyond the border, day or night, in clear weather or in fog and smoke. Their ability to see what the human eye cannot is what makes these systems especially valuable.
An EO (electro-optical) camera can be thought of as a much more advanced version of the camera in your phone. It captures sunlight — or light from any source — reflecting off objects and turns it into a sharp, colored image.
During the day or in well-lit conditions, an EO camera performs extremely well: it can clearly make out a vehicle's license plate, a person's clothing, even their face. But it has one weakness — it's useless in the dark. Without enough light, an EO camera can't see anything either.
This is where thermal cameras come in. Thermal cameras don't look at light — they look at heat. Every living being and every object radiates some amount of heat into its surroundings; a thermal camera detects that heat and converts it into an image.
The big advantage here is that it needs no light at all. Even in pitch darkness, it can easily pick up a person's body heat or the engine heat of a vehicle that has just been switched off. It can also, to some degree, "see through" obstacles like fog, smoke, or dense tree cover, because heat waves pass through these obstacles more easily than visible light does.
Thermal cameras themselves come in two main types:
LWIR (Long-Wave Infrared): Captures a general heat signature — how much hotter or cooler an object is compared to its surroundings. Ideal for long-range detection.
MWIR (Mid-Wave Infrared): Picks up very high-temperature points more sharply — for example, a recently running engine or a weapon's muzzle. Preferred when finer target discrimination is needed.
On top of these, there's also SWIR (Short-Wave Infrared). This produces an image very similar to a regular camera, but it's less affected by fog and humidity, and can even see a person sitting behind glass.
A drone doesn't stay perfectly still in the air — it's constantly shaken by wind. This shaking makes it nearly impossible for a camera to stay locked onto a target. That's why all these cameras are mounted on a special housing called a "gimbal."
The gimbal instantly detects every jolt the drone experiences and moves in the opposite direction to keep the camera stable. The result: even highly zoomed-in footage from long distances can be tracked steadily, without shaking. Without a gimbal, even the most advanced camera would be practically useless.
Modern drones don't rely on a single camera. Multiple sources of information are gathered at the same time — EO footage, a thermal heat map, sometimes radar data. A process called "sensor fusion" combines all these different data streams into a single, coherent picture.
The benefit is simple: a single sensor can be wrong. For example, an EO camera might mistake a snowy patch of terrain for a group of people from a distance. But the system simultaneously checks the thermal camera; if there's no human-temperature heat signature there, it automatically filters out that false alarm. This makes the information presented to the operator far more reliable.
In the past, an operator had to manually lock the camera onto a target and continuously track it by hand. Now, AI-powered systems can automatically recognize and lock onto a person, vehicle, or other target, and keep tracking it on their own even as it moves.
This changes the operator's job: instead of manually steering the camera, they can now focus on evaluating the information the system provides and making decisions.
To make all these pieces concrete, let's walk through a simple scenario:
As the drone scans the border area from high altitude, its thermal camera notices an unexpected heat spike in a distant valley. This triggers the system into alert mode.
The gimbal locks the camera onto that point; the EO and SWIR cameras kick in to provide a clear image. AI determines the target is a group of people and begins automatic tracking.
A laser rangefinder calculates the distance to the target with an accuracy of a few meters (typically within ±1–5 meters). This is far more reliable than estimates based on the naked eye or a map, and it's used to assess how urgent the situation is.
If the target is assessed as a genuine threat, a laser designator can lock onto it, guiding precision-guided systems toward the target.
This entire process unfolds within seconds and feeds real-time information back to command.
In this field, Türkiye's standout product is the ASELFLIR-500, developed by ASELSAN. Designed for electro-optical reconnaissance, surveillance, and targeting, this system is integrated into several of Türkiye's domestically produced drones, most notably the Bayraktar AKINCI combat UAV, as well as the Bayraktar TB3 and Bayraktar TB2 and TUSAŞ ANKA-3.
The system combines infrared, daylight, and SWIR cameras, along with a laser rangefinder and a laser designator (35 km range), all within a single housing. All these cameras share a common 220 mm optical aperture, which translates into higher image quality and longer range. AI-powered image processing also allows it to track multiple targets simultaneously and automatically calculate the direction and speed of moving targets.
Explore ASELFLIR-500 in more detail ↗
These systems aren't perfect. Heavy rain, fog, or dust can degrade the performance of both EO and some infrared cameras. Trained individuals can partially conceal themselves using natural camouflage or heat-insulating materials. And the final decision still rests with a human — AI helps, but it doesn't eliminate the margin for error entirely.
Looking ahead, lighter and smarter sensors, drone networks that communicate with one another, and systems that form an uninterrupted surveillance ring along the border are all expected to become more widespread. This points toward border security becoming far more automated and integrated in the coming years.