Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition

Abstract

Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor image comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a training accuracy of 99%, a testing accuracy of 89.73%, a precision of 90.11%, a recall of 89.73%, and an F1-score of 89.79%. These findings underscore the efficacy of our methodology in tackling the intricacies of indoor scene recognition. This research substantially advances the domain of robotics and automation by establishing a more resilient and dependable framework for autonomous navigation and scene comprehension in GPS-denied settings, facilitating the development of more efficient and intelligent robotic systems.