RfAntiDrone SDK is a set of software libraries for optoelectronic detection systems of unmanned aerial vehicles (UAVs), which solves the problem of automatic detection and automatic tracking of UAVs. The SDK is designed for use in all types of processor platforms and allows developers and manufacturers to equip their systems with UAV detection and tracking functions with proven efficiency. Manufacturers of detection systems receive easy-to-integrate and proven algorithms, operational technical support and constant updates, which allows to effectively promote the developed system on the market. RfAntiDrone SDK significantly reduces time and risks for the development of UAV detection systems.


SOFTWARE

  • RfMotionDetector lib is C++ software library for automatic detection of any moving objects on video. It is used to detect UAV when the camera is stationary (or with small smooth movements), as well as to automatically search for UAV after the turn of the optical system on the external target designation (for example, from radar). It is able to detect moving objects of very small size and low contrast.

  • RfBirdClassifier neural net is a neural network for checking the presence of birds in images. It is used together with the library RfMotionDetector lib to check the detected objects for being birds or not. It allows to significantly reduce the probability of false alarms of the system on birds, which is especially important when detecting remote objects. Objects detected by the motion detector (their images) are transmitted to the neural network, which in turn refers it to one of two classes: "bird" or "other". Neural network calculation can be performed using any of the freely available frameworks (e.g. OpenCV).

  • RfDroneDetector neuron net – neural network for UAV detection on video frames. Used separately to detect UAVs on each video frame, regardless of camera movement. It detects objects from the moving platform (in motion or with rotating cameras). Neural network calculation can be performed using any of the freely available frameworks (e.g. OpenCV).

  • RfVideoTracker lib is C++ software library for automatic tracking of objects. It is used for high-precision tracking of detected objects (both UAVs and any other). The library provides guidance of the optical system to the object for continuous calculation of its exact coordinates and their further transmission to the client (suppression or destruction system).


interaction of components

In a typical UAV detection system, the primary detection is performed by the radar after which the coordinates of the detected object in the form of target information are transmitted to the rotary platform of the cameras. Cameras are needed to confirm the UAV detection and accurate tracking in order to calculate its exact coordinates in space. After turning the camera on the target indicated by radar, UAV detection is performed already on video using the RfMotionDetector lib library. All objects detected by the RfMotionDetector lib library are checked for being birds or not using the neural network RfBirdClassifier neural net. The RfMotionDetector lib library requires several video frames to detect UAVs, and it is possible to detect very small objects (4x4 pixels). If it is necessary to detect the UAV on the first frame after the turn, as well as in conditions of constant movement of the camera, then the neural network RfDroneDetector neural net is used for this purpose. If UAV detection is confirmed, we lock on the UAV for automatic tracking by RfVideoTracker lib library. After the licking on UAV for automatic tracking, RfVideoTracker lib library provides a continuous (frame to frame) generation of coordinate data of UAV (the coordinates of the UAV on the video frames), which is the basis for continuous tracking (turn camera to the object). During automatic tracking on the basis of information about the position in the direction of rotation of cameras (azimuth and elevation sensors data are read), the UAV positioning data is generated for the client (suppression or destruction system).

Primary UAV detection can be carried out using cameras (in visible or infrared range). This library is used RfMotionDetector lib that allows you to detect objects at a great distance. If the detection system includes circular motion cameras, the UAVs are detected using the neural network RfDroneDetector neuron net, and the size of the detected objects (on video frames) should be larger than for the library RfMotionDetector lib.


price

The price of a perpetual license for the RfAntiDrone SDK without source code and training samples of neural networks is 15860 EUR under SSBL license. The price of a set of source codes and training samples for neural networks is 104000 EUR under SSCL license.


main parameters

RfMotionDetector lib — motion detector:

  • Detection of objects from 4x4 pixels in size.

  • Detection of objects of very low contrast against the interference background.

  • Filtering of vegetation and birds movement.

  • The number of simultaneously detected objects is not limited.

  • Processing of intersecting trajectories.

  • Ability to work in low-power embedded processors.The same efficiency in both visible and infrared video.

RfVideoTracker lib — video tracker:

  • Stable tracking of objects from 4x4 pixels in size.

  • Resistance to the overlap of the object, changing its angle and dimensions (on video).

  • Tracking objects of very low contrast against interference.

  • Processing up to 1000 frames per second.

  • Ability to work in low-power embedded processors.

  • Same efficiency in both visible and infrared video.

RfBirdClassifier neuron net — neural network to check the presence of birds in images:

  • Recognition of birds on the image size of 10x10 pixels.

  • The probability of correct recognition is 85%.

  • High-speed operation.

  • Ability to work in low-power embedded processors.

  • The neural network gives the sign "bird"or " other".

  • The same efficiency in both visible and infrared video.

RfDroneDetector neuron net – neural network for UAV detection.

  • UAV detection size from 16x16 pixels.

  • Processing of the whole frame. The neural network gives the position of the UAV on the processed frame and the probability of belonging to the class of UAV (from 0 to 1).

  • The same efficiency in both visible and infrared video.


DOWNLOADABLE FILES

write to us

Name *
Name