The document discusses the use of UAVs (unmanned aerial vehicles) in object detection, highlighting their autonomous capabilities and sensor technology. It emphasizes the importance of object detection for tasks such as navigation, surveillance, and search and rescue, along with the AI technologies used, including machine learning and deep learning models. Challenges in UAV object detection include limited processing power, environmental factors, and real-time processing requirements.

1. Ministry Of Higher Education And
Scientific Research
University Of Diyala
College Of Engineering
Department Of Communication
UAV Object detection using Al technology
Prepared by students
‫وليد‬ ‫العزيز‬ ‫عبد‬
‫ناهض‬ ‫زينب‬
Supervised by
‫جاسم‬ ‫علي‬ ‫حيدر‬ ‫دكتور‬

2. Introduction
 A UAV (Unmanned Aerial Vehicle), commonly known as a drone, is an aircraft that
operates without a human pilot onboard. It can be controlled remotely by an operator or
autonomously via onboard computers and sensors.
 Key Characteristics of UAVs:
 Autonomous or Semi-autonomous: Operate based on pre-programmed instructions or
real-time commands.
 Equipped with Sensors: Cameras, GPS, LiDAR, thermal sensors, etc.
 Variety of Sizes: From small hobbyist drones to large military-grade UAVs.
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3. Types of UAVs
• Fixed-Wing UAVs:
• Long endurance and range (e.g., surveillance
drones).
• Rotary-Wing UAVs:
• High maneuverability (e.g., quadcopters).
• Hybrid UAVs:
• Combine fixed and rotary-wing capabilities.

5. importance of Object Detection for UAVs
Object detection is a critical capability for UAVs because it allows
drones to identify, classify, and track objects in their environment.
This enhances their ability to perform tasks autonomously and safely.
Object Detection is Important:
1. Navigation and Collision Avoidance:
2. Surveillance and Monitoring:
3. Search and Rescue:
4. Precision Agriculture:
5. Delivery Services:
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6. AI Technologies in Object Detection
• Machine Learning (ML): Traditional
algorithms (SVM, k-NN).
• Deep Learning (DL): Convolutional
Neural Networks (CNNs).
• Popular frameworks: TensorFlow,
PyTorch, YOLO

7. AI Technologies in Object Detection
Steps Involved:
1. Image Capture via onboard cameras.
2. Preprocessing: Enhancing image quality.
3. Feature Extraction using CNNs.
4. Classification and Localization.
- Real-Time Processing on UAV hardware.

8. Deep Learning Models for UAV Object
Detection
o Popular Models:
 YOLO: Real-time detection.
 SSD (Single Shot Multibox Detector): Faster
computation.
 Faster R-CNN: High accuracy.
• Comparison Table:
o Speed vs. Accuracy.
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9. Hardware Considerations for UAVs
• Onboard Processors: NVIDIA Jetson, Intel Movidius.
• Cameras: RGB, Infrared, Thermal.
• Power Constraints: Battery efficiency for AI processing.
• Image: UAV with annotated components (camera, processor).

10. Challenges in UAV Object Detection
• Limited Processing Power: Edge computing challenges.
• Environmental Factors: Lighting, weather, occlusions.
• Latency: Real-time constraints.

14. Thank You