Enhancing Transparency in AI: An Explainable Deep Learning Approach for Computer Vision Systems

Srinivas Konduri, KVLN Raju

Abstract: Artificial Intelligence AI systems, particularly those based on deep learning, have shown remarkable performance in various computer vision tasks. However, their opaque nature often raises concerns about their interpretability and transparency. This paper presents a novel Explainable Deep Learning model that addresses these concerns by providing insights into the decision-making mechanisms of AI models. The model, implemented using OpenCV, incorporates techniques such as Grad-CAM and LIME to justify its predictions, thereby enhancing transparency and fostering trust in AI systems. The models performance and interpretability are evaluated using benchmark datasets, demonstrating its effectiveness in generating human-comprehensible explanations. This research contributes to the field of Explainable AI by offering a practical solution to the trade-off between high-performance AI systems and transparent decision-making.

Keywords: Dynamic traffic lights, OpenCV, Deep Learning, Grad-CAM, LIME, DenseNet-121