Improved Human Detection and Classification using Supervised Machine Learning Algorithms

In many applications, surveillance, robots, and autonomous driving, human detection is an essential role. Deep learning techniques have made significant strides in recent years when it comes to quickly and accurately detecting humans. This research proposes a deep learning-based investigation into human detection. This study specifically concentrates on how well YOLO, SSD, and Faster R-CNN perform among other cutting-edge deep learning models for human detection. Using benchmark datasets, these models are assessed and their advantages and disadvantages are considered. Furthermore, by adding contextual information into the detection process, this study suggests a unique method for enhancing human detection accuracy in low-light situations. The proposed tests demonstrate that the proposed strategy outperforms other existing approaches. Aerial picture detection, multi-person detection, and real-time detection are some of the issues and future directions are discussed in the final section. The research findings show how deep learning may be used to perform human detection and offer guidance for further study in this field.