ANALYSIS AND IMPLEMENTATION OF YOLOV7 IN DETECTING PIN DEL IN REAL-TIME

Iustisia Natalia Simbolon; Daniel Fernandez Lumbanraja; Kristina  Tampubolon

doi:10.52436/1.jutif.2024.5.2.1286

Iustisia Natalia Simbolon Informatics, Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Indonesia
Daniel Fernandez Lumbanraja Informatics, Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Indonesia
Kristina Tampubolon Informatics, Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.2.1286

Keywords: Confidence Threshold, IoU Threshold, Real-time, Recall, YOLOv7

Abstract

Real-time object detection is the process of identifying and tracking objects instantly and directly without any delay between image input and output. Carrying out real-time detection is a challenge in detection systems because it requires speed and accuracy of detection. This research proposes the application of the YOLOv7 algorithm which allows object localization and classification in one stage. This detection is carried out in real time on two objects, namely PinDel and Students. This research focuses on applying the YOLOv7 algorithm to detect real-time use of Pin Del by students. In this research, several hyperparameters were adjusted until the optimal value was found, including epoch with a value of 300, as well as confidence threshold, and IoU threshold with a value of 0.5. The model evaluation results from hyperparameter experiments show good results, with precision of 0.946, recall of 0.959, and mAP@0.5 of 0.977. This research has succeeded in detecting Pin Del objects in real time by obtaining a detection speed of between 7 and 40 FPS, which shows a fast response in detecting objects in real time. This research has contributed to the development of real-time object detection technology and its application in Pin Del use cases by students.

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