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Abstract

Currently, rapid growth in airports is encountered. Thus, enhancing customer experience, safety, and satisfaction using advanced technologies, such as deep learning (DL), is important. This research study explores the available DL and image processing algorithms to identify the most suitable algorithms that provide high detection accuracy to support the lost and found units in airports. Using the collected data from the surveillance camera technology in the airports, the performance of the algorithms is benchmarked and evaluated on an identical dataset to acquire an understanding of the distinctive features of each algorithm, the ability to distinguish between the algorithms and choose the approach to object identification that works best in each situation. Faster R-CNN and YOLO-v8 algorithms are the focus of this study by exemplifying the various abilities of both and identifying their suitability to be used in individual and object identification in airports to support lost and found units. YOLO-v8 is a better fit for the airport lost and found units to help passengers allocate their lost bags than Faster R-CNN. This is evident from the evaluation results of measuring the accuracy and speed of both algorithms, in which YOLO-v8 performed significantly better. Considering the focus of this research and the need for real-time object detection, YOLO-v8 is a superior fit for airport use as the analysis results are real-time, which is preferable in such an industry.

Keywords

deep learning, faster R-CNN, YOLO-v8, airports, lost and found unit

Article Type

Article

First Page

9

Last Page

21

Publication Date

4-30-2025

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