Real-Time Pedestrian Detection Using Enhanced Representations from Light-Weight YOLO Network
Shayan Shirahmad Gale Bagi, Behzad Moshiri, Hossein Gharaee Garakani, Mark Crowley, Pouya Mehrannia · 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT) · 2022
Pedestrian detection is one of the significant tasks in Autonomous Vehicles (AVs). There are two kinds of networks which are widely used for pedestrian detection: single-stage networks and region-based networks. Single-stage networks, such as YOLO, solve the bounding box regression and classification problems simultaneously which makes them faster than region-based networks such as Faster R-CNN. Nonetheless, the main structure of YOLO is too complex and slow for the pedestrian detection task in AVs and cannot detect small pedestrians. Furthermore, unlike region-based networks where all features of the region containing a pedestrian is used in classification, in YOLO only the features of a cell in which the center of anchor box lies is used in classification. In this paper, these issues related to YOLO will be addressed such that it can be better used for pedestrian detection.