Human Activities Detection using DeepLearning Technique- YOLOv8

Abstract

Using a mask during the pandemic has occasionally been crucial and difficult. The use of universal masks can greatly lower and possibly even stop the spread of viruses within communities. So, mask detection has become a very critical task for security agencies in all the buildings, Government offices & other places. With the advent of GPUs, high computing machines, and Deep Convolution Neural Networks (DCCN), automatic Face & Mask Detection is possible by considering the image processing feature of extracting, 3-dimensional shapes from 2- dimensional images. This paper discuss about the YOLOv8 model to confirm its overall applicability, on two datasets namely FDDB & MASK. This helps to examine the behavior of the feature from the Mask dataset, which is intended for COVID-19 Mask Detection alone. Mask is the main dataset in this experiment. Above this, the ImageNet dataset is utilized for pretraining and FDDB (Face Detection Dataset & Benchmarks) datasets for recognizing face of a human being. The precision of models on FDDB is 58.9 % & on MASK dataset is 66.5%.

Performasi Deteksi Jumlah Manusia Menggunakan YOLOv8

“…Kelebihan YOLO dibandingkan dengan algoritma lain adalah real time processing yang cepat dan efisien serta akurasi yang dapat dipertimbangkan baik. Salah pengembangan YOLO yang terbaru adalah versi 8, yang memliki kemampuan kecepatan, akurasi, dan efisiensi dalam deteksi objek dibandingkan dengan versi yang lama [2], [3]. Penelitian Yang (2023) dalam mendeteksi buah tomat menggunakan yolov8 memberikan loss yang rendah 93,4% mAP dari model data latih serta peningkatan 2% nilai presisi dan recall rate meningkat 0,8% dibandingakn versi lama [4].…”