Implementation of YOLOv7 Model for Human Detection in Difficult Conditions

DOI: https://doi.org/10.33650/jeecom.v7i1.10662

Authors (s)


(1) * Arijal B   (Universitas AMIKOM Yogyakarta)  
        Indonesia
(2)  Andi Sunyoto   (Universitas AMIKOM Yogyakarta)  
        Indonesia
(3)  M. Hanafi   (Universitas AMIKOM Yogyakarta)  
        Indonesia
(*) Corresponding Author

Abstract


The rapid development of artificial intelligence technology in recent decades has led to the development of highly efficient object detection algorithms, including human detection under difficult conditions. Human detection is one of the major challenges in computer vision as it involves various complex factors such as obstructed human objects, pose variations, small low-resolution human objects, as well as the presence of fake human objects such as statues or images. This research uses the SLR (Systematic Literature Review) method to determine the algorithm used, namely YOLOv7. The three YOLOv7 models tested in this study are YOLOv7x.pt, YOLOv7-w6-person.pt, and YOLOv7-w6-pose.pt. These models were selected based on their excellence in detecting human objects and their relevance for complex scenarios. Tests were conducted using 100 images obtained from the internet and divided into four categories of human objects under difficult conditions, which represent various challenges in human detection. Analysis was performed using convusion matrix to evaluate performance metrics such as accuracy, precision, recall, and F1-score. Based on the test results, the YOLOv7-w6-person.pt model showed the best overall performance, especially in detecting humans in obstructed conditions and complex lighting with a precision of 90.4%, Recall 88.7%, and F1-Score 89.5%. This model has higher accuracy, precision, and F1-score than the other models, making it a reliable choice for human detection in difficult scenarios. These findings not only demonstrate the relevance of YOLOv7 as a reliable human detection algorithm, but also provide a basis for further optimization of YOLOv7-based human detection systems, both through improving the model architecture and adapting to more specific datasets. This research makes an important contribution to the development of human detection technologies for real-world applications, such as surveillance, crowd analysis, and automated safety systems.



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Journal of Electrical Engineering and Computer (JEECOM)
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