Introduction
Airports stand as bustling hub, with arrivals and departure of planes across different destination. The dynamic nature of air traffic demands a comprehensive understanding of patterns, operational efficiency, and various other factors influencing airport dynamics. The increasing complexity and volume of air traffic necessitate innovative solutions to enhance the precision and agility of air traffic control systems. In air traffic control, computer vision aids in monitoring and managing the movement of aircraft. For this project, I employed object detection using the YOLO (You Only Look Once) algorithm to monitor and analyze the aircraft movement at airports.
Methodology
YOLOV8, is a new state of the art computer vision model, developed by Ultralytics that support object detection classification and segmentation tasks. It is able to achieve a strong accuracy on COCO (Common Objects Context) dataset. The initial step includes loading the pre-trained YOLOV8 model. The pre-trained model, trained on the COCO dataset, serves as a robust foundation for detecting various objects in complex scenes.
In this project, my use-case works for any video time-lapse as input, which is processed to extract individual frames, converting the temporal sequence into a collection of static images and applying object detection to identify and locate flights within each frame.
The YOLOV8 model, trained on the COCO dataset, proves effective in detecting airplanes in diverse scenarios. The source of the videoclip doesn’t provide any timestamp, hence, the results are mapped to image labels for analysis.
The model predicts the presence and location of airplanes within each image. I compiled these results of each object detection iteration, including the count of detected flights and detailed information for each detection, into a structured dataframe for analysis.
Results and analysis
Analyzing a sample video input from Kuala Lumpur airport in a day, 24 frames of images were generated. The resulting bar plot visualizes the frequency of detected flights in each image, providing a temporal understanding of air traffic patterns.
In the daytime frames, there is a higher concentration of flights, indicated by peaks in the bar plot. As the timeline progresses towards the night, the frequency slightly decreases, suggesting a potential correlation between air traffic and time of day. The declining trend in flight frequency during nighttime frames aligns with expectations of reduced air activity during these hours.
The histogram plot of detection probabilities shows insights into the distribution of confidence levels across all detected flights.
The concentration of detections in lower confidence intervals suggests that the YOLOV8 model may face challenges in accurately interpreting certain objects in the airport environment. This could be attributed to challenging scenarios such as poor lighting, occlusions, or complex backgrounds.
Here is an example output of an image frame annotated with bounding boxes highlighting the detected objects.
Analysis of Vision Algorithms
While the model performed well in well-lit scenarios, its effectiveness decreased during nighttime or low-light conditions. This is a common challenge for computer vision algorithms, and YOLOV8 is no exception. Moreover, the quality of input images significantly influenced the accuracy of object detection as observed in the displayed result where some planes on the left remained unidentified. Despite occasional limitations, this system was able to detect airplanes in clear conditions, providing useful insights for the analysis.
Conclusion
The implementation of YOLOV8 for airplane detection offers a powerful tool for understanding air traffic dynamics. The model showcases strong performance in well-lit conditions, providing valuable insights into daytime operations. While there is room for improvement, the project underscores the potential of YOLOV8 in air traffic analysis using Object Detection for various aspects of airport operations, safety, and efficiency.
Github link : https://github.com/Aparna003/Object_detection_system