Multi‐scale pedestrian detection with global–local attention and multi‐scale receptive field context

Abstract As a basic component in the field of computer vision, the pedestrian detection plays an essential role in several real‐world applications such as video surveillance. The promising performance has been achieved in pedestrian detection relying on deep learning, but large‐scale variance and small‐scale pedestrian detection remain inherently hard as before. In order to deal with the aforementioned problems, this paper proposes a multi‐scale pedestrian detection method with global–local attention and multi‐scale receptive field context (MRFC). To make the network focus on small‐scale pedestrians, we add a high‐resolution detection branch on the original detector. To better integrate the incongruous semantic feature, the global–local attention module is embedded to highlight the feature representation of pedestrians so as to implement the feature fusion effectively. In order to adapt the receptive field of the network to achieve scale‐variance detection, the MRFC is applied. Based on integrating the above structures, the proposed method achieves competitive results on Caltech and CityPersons datasets. The source code is released in https://github.com/xiaopan999/yolov5‐pedestrian_detection.