Automated Human Detection in Images Using Deep Learning

Abstract

Human detection is a vibrant and ever-evolving field of active research in the present context. This paper presents an automated approach utilizing deep learning techniques to identify and track humans within images.  The primary objective of this work is to detect individuals within images. Machine learning models have found extensive utility in a variety of applications, including autonomous vehicles and speech recognition. In this paper, we introduce a deep learning-driven method for automated human detection, harnessing the power of convolutional neural networks (CNNs) and state-of-the-art object detection architectures. To boost the performance of our deep learning-based human detection system, we employ several strategies. These encompass the use of data augmentation techniques to expand the dataset, harnessing transfer learning to leverage pre-trained models, and implementing advanced optimization algorithms for model fine-tuning. Additionally, we investigate the effectiveness of different network architectures, loss functions, and hyperparameters to optimize detection accuracy. Our experimental results illustrate the effectiveness of deep learning in automating human detection. Our proposed approach attains high precision and recall rates, effectively pinpointing humans in a variety of challenging scenarios. The potential applications of our method span diverse domains, including surveillance systems, activity recognition, and human-robot interaction.