Advancements in Pedestrian and Vehicle Detection: A Comprehensive Review of Deep Learning Techniques

Detecting pedestrians and vehicles is a critical task in applications such as traffic management, surveillance, and autonomous driving. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have significantly improved object detection, enabling revolutionary progress in the field. This paper reviews state-of-the-art techniques for pedestrian and vehicle detection, tracing their evolution from traditional feature-based approaches to advanced deep learning architectures such as YOLO, SSD, and R-CNN. Key datasets, including COCO, KITTI, and Cityscapes, are examined for their relevance in training and evaluating these models, alongside performance metrics like Mean Average Precision (mAP) and Intersection over Union (IoU). The paper identifies key challenges, such as detecting small and occluded objects, achieving real-time performance, and ensuring transferability across diverse domains. It emphasizes the importance of addressing scalability issues and deployment constraints, particularly for embedded systems and resource-limited environments. Emerging technologies, including attention mechanisms and transformer-based models, are explored for their potential to enhance detection capabilities. By integrating discussions on practical applications and real-world deployment, this paper bridges the gap between theoretical advancements and industry needs. It provides insights into future research directions, such as multi-modal data fusion and robust algorithms, to inspire innovation in pedestrian and vehicle detection systems.