Pedestrian Detection: An Explainable Approach

In the rapidly advancing field of autonomous technologies, particularly in transportation and surveillance, there is a critical need for accurate and reliable pedestrian detection systems. While deep learning models have achieved remarkable success in this area, their “black box” nature often concerns their trustworthiness and practical applicability. Our study introduces a modified YOLOv5s model, augmented with a Global Attention Mechanism and enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM) for improved explainability. This approach maintains the high accuracy typical of deep learning models while significantly increasing their transparency. Our modified YOLOv5s architecture is tailored for real-time applications, achieving an inference speed of 9.4 milliseconds on a single NVIDIA A100 GPU. Incorporating Grad-CAM provides a more intuitive interpretation of the model's decisions, potentially increasing user confidence in its predictions. We evaluate our enhanced framework on the CityPersons dataset, demonstrating improvements in precision, recall, and [email protected] compared to the baseline YOLOv5s model. Our results indicate a promising advancement in pedestrian detection, balancing high performance with improved explainability.