Deep Learning-Based Object Detection Across Multiple Visual Domains

The study aims to present a deep learning-based approach for object detection across multiple visual domains, addressing the challenges of varying contexts, lighting conditions, and different appearances of objects. To this end, we implement the Faster R-CNN algorithm, which is well renowned for its efficiency in detection tasks. The model is trained on a multidomain dataset, consisting of urban, rural, indoor, and natural scenes that ensure robustness across diverse environments. To improve domain adaptability, we use domain adaptive techniques to allow the model to share knowledge across domains efficiently. Experimental results show that the Faster R-CNN model attains an average 94.2% of accuracy in object detection across all its domains. This result gives a very significant lead on traditional methods. For testing in individual domains, results show 96.8% accuracy for the urban scene of urban, 93.1% for rural, and 92.4% for indoor. This opens avenues for efficient, real-world object detection applications in complex and varied visual environments.