Artificial Vision System for Autonomous Mobile Platform Used in Intelligent and Flexible Indoor Environment Inspection

Abstract

The widespread availability of artificial intelligence (AI) tools has facilitated the development of complex, high-performance applications across a broad range of domains. Among these, processes related to the surveillance and assisted verification of indoor environments have garnered significant interest. This paper presents the implementation, testing, and validation of an autonomous mobile platform designed for the intelligent and flexible inspection of such spaces. The artificial vision system, the main component on which the study focuses, was built using a Raspberry Pi 5 development module supplemented by a Raspberry Pi AI Kit to enable hardware acceleration for image recognition tasks using AI techniques. Some of the most recognized neural network models were evaluated in line with the application’s specific requirements. Utilizing transfer learning techniques, these models were further developed and trained with additional image datasets tailored to the inspection tasks. The performance of these networks was then tested and validated on new images, facilitating the selection of the model with the best results. The platform’s flexibility was ensured by mounting the artificial vision system on a mobile structure capable of autonomously navigating indoor environments and identifying inspection points, markers, and required objects. The platform could generate reports, make decisions based on the detected conditions, and be easily reconfigured for alternative inspection tasks. Finally, the intelligent and flexible inspection system was tested and validated for its deep learning-based vision system performance.