Pedestrian detection technology is an important research direction for the development of intelligent transportation and intelligent vehicles, and it is also an important guarantee for road safety, which directly affects the judgment of a vehicle control system on road conditions. In practical application scenarios, small-scale pedestrian instances account for a very high proportion, but small-scale pedestrian detection has always been a challenging problem in pedestrian detection tasks. When an intelligent vehicle is in a complex traffic environment, the precise detection of small-scale pedestrians can make the control system give a warning in advance and help avoid collision in time, which plays an important role in ensuring the safe and stable driving of the vehicle. With the rapid development of deep learning, groundbreaking progress has been made in the fast-growing small-scale pedestrian detection technology. To further promote the development of small-scale pedestrian detection technology, this study conducts comprehensive research on the latest methods of small-scale pedestrian detection technology. To start with, this study analyzes several challenges faced by small-scale pedestrian detection and classifies and summarizes the latest small-scale pedestrian detection networks. The existing deep learning methods are analyzed and discussed from five aspects, namely multi-scale representation, context information, new training and classification strategies, scale perception, and super-resolution. Among them, the multi-scale learning method is the mainstream of small-scale pedestrian detection. Meanwhile, we briefly introduce the commonly used evaluation indicators and datasets for pedestrian detection and evaluate the performance of some mainstream methods on general datasets such as Caltech. In addition, five methods are summarized and compared in this study. Finally, this study proposes the urgent problems to be solved in pedestrian detection technology and the direction and tasks of future development from multiple aspects.