Airguard Ai: Revolutionizing Air Cargo Inspection through Pygame and YOLOv8 Simulation

Abstract

The objective of this project is to create a simulation for air cargo inspection using artificial intelligence (AI). This will be achieved by combining Pygame for simulating conveyor belts and YOLOv8 for detecting hazardous items. The simulation replicates a conveyor belt system that transports air cargo for inspection. Pygame, a robust game development package, is used to build a graphically interactive environment where users may view and examine the cargo inspection process in a simulated context. The primary element of the project entails using YOLOv8, an advanced object detection model, to precisely locate dangerous things within the shipment. The real-time detection capabilities of YOLOv8 allow for quick and efficient examination of the cargo, ensuring the timely identification of any hazards. The simulation serves as a platform to assess and verify the effectiveness of the AI-driven cargo inspection system across different scenarios. Users have the ability to engage with the simulation by modifying factors like the speed of the conveyor belt, the types of cargo, and the criteria for inspection. This allows them to assess the effectiveness of the system in identifying dangerous objects. This project functions as an instructional tool to comprehend the incorporation of AI in cargo inspection, while also having practical implications for improving real-world air cargo security. Pygame and YOLOv8, when combined, offer a flexible and robust framework for simulating and evaluating AI-based inspection systems. This contributes to the progress of air cargo safety and security measures.