Advanced Threat Detection: Enhancing Weapon Identification and Facial Recognition with YOLOv8

Abstract

The provided implementation code integrates several computer vision and machine learning techniques to detect and analyze the presence of persons and weapons in an image, utilizing pose estimation to further assess the relationship between detected persons and objects. It employs convolutional neural networks (CNNs), specifically a custom model for person detection and YOLO (You Only Look Once) for weapon detection. The pose estimation is performed using the Media Pipe library, which helps in identifying human poses by locating key points on the person's figure depicted in the image.

The process initiates by loading the necessary models and preparing the image for detection tasks. Persons in the image are identified using a custom CNN model that processes images pre-processed to highlight facial features, while weapon detection is handled by YOLO, which scans for items classified as weapons. Following the detection, the application determines the spatial relationship between detected weapons and persons by analyzing key points from the pose estimation to see if a person is holding a weapon.

Keywords- computer vision, machine learning, pose estimation, object detection, CNN, YOLO, MediaPipe, facial recognition, weapon detection, image processing, security applications, real-time analysis, convolutional neural networks, keypoints detection.