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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Acuna-Condori, Kevin
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Computer Science & IT Engineering

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Security in smart cities using YOLOv8 to detect lethal weapons Rodriguez-Rosas, Ederson; Castillo-Turpo, Aron; Acuna-Condori, Kevin; Paiva-Peredo, Ernesto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp945-953

Abstract

The increase in the illegal use of lethal weapons at a global level has reachedworrying figures, resulting in an increase in assaults and armed robberies. Based on the above, closed circuit television (CCTV) surveillance systems emerge as an alternative solution. Therefore, the use of artificial intelligence is explored in order to detect the presence of lethal weapons in images accurately. In this study, a convolutional neural network model YOLOv8 is trained. A database including 4104 images with the presence of lethal weapons is generated. The Google Colab platform is used for the training phase, since it provides us with a free graphic processing unit (GPU), and the YOLOv8x and YOLOv8n models are used for comparison. The results indicate an advantage when using the YOLOv8 models, and when comparing them with similar models already proposed in the studied literature, we can conclude that our model stands out with an accuracy of 89.56% in the detection of lethal weapons. In conclusion, we were able to obtain a model capable of detecting lethal weapons in CCTV images, in addition to being able to be used in applications that require real-time detection. 
Real-time age-range recognition and gender identification system through facial recognition Cruz-Colan, Carlos; Lopez-Herrera, David; Paiva-Peredo, Ernesto; Acuna-Condori, Kevin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp992-999

Abstract

Facial recognition and age estimation are being implemented in apparel retailing which is undergoing significant changes due to fashion and technology. To improve interaction with customers and refine marketing strategies. The paper proposes an approach based on a Siamese neural network and the use of tools such as MediaPipe for face detection and DeepFace for age and gender estimation. In addition, the four stages of the research work, real-time image capture, ID assignment, facial feature extraction, and data storage, are described. Early approaches to age estimation were based on biometric features, such as eyes, nose, mouth, and chin, resulting in limited accuracy and low performance in older adults. To improve accuracy, additional elements, such as the presence of wrinkles, were considered and a diverse database of images was used. The proposed methodology achieves a positive result for real-time age classification and gender ID. The results include information on gender, age, ID, time and date for each person identified.
Co-Authors Castillo-Turpo, Aron Cruz-Colan, Carlos Lopez-Herrera, David Paiva-Peredo, Ernesto Rodriguez-Rosas, Ederson