Object detection in UAV (Unmanned Aerial Vehicle) cameras refers to the process of identifying and locating specific objects—such as people, vehicles, buildings, or terrain features—within the video or image data captured by the UAV’s onboard camera systems. This capability is essential for applications in surveillance, military targeting, disaster response, agriculture, and infrastructure inspection.

How It Works

1. Image Acquisition
   UAVs capture video or still images using electro-optical (EO), infrared (IR), thermal, or multispectral cameras, often mounted on stabilized gimbals to maintain steady views during flight.

2. Preprocessing
   Captured frames may be enhanced, resized, or filtered to improve detection quality. GPS and IMU data may also be fused for geo-referencing.

3. Object Detection Algorithms

   • Traditional methods:

     • Histogram of Oriented Gradients (HOG) + SVM

     • Haar cascades

     • Background subtraction (motion-based detection)

   • Deep learning-based methods (dominant in current systems):

     • YOLO (You Only Look Once) – real-time object detection

     • SSD (Single Shot Detector) – efficient and accurate

     • Faster R-CNN – more accurate but slower

     • DETR (DEtection TRansformer) – advanced transformer-based detector

4. Post-Processing

   • Track objects across multiple frames using Kalman Filter, SORT, or DeepSORT

   • Filter false positives, apply confidence thresholds

   • Combine detection output with telemetry and map overlays

5. Data Transmission or Storage

   • Detections and metadata can be stored onboard or sent via telemetry links (e.g., LTE, CUAV P9, or LoRa) to ground control stations

Common Hardware for UAV Object Detection

• Cameras:

  • RGB (EO), IR, thermal, or multispectral cameras

• Processing Units:

  • NVIDIA Jetson series (Nano, TX2, Xavier, Orin)

  • Google Coral TPU

  • Intel Movidius Neural Compute Stick

  • FPGA or high-end STM32 MCUs (for pre-filtering or simple logic)