Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection, it remains very challenging to leverage this method for real-time pedestrian detection. In this paper, we propose a new framework called SA YOLOv3, a scale-aware You Only Look Once framework which improves YOLOv3 in improving pedestrian detection of small scale pedestrian instances in a real-time manner.

Our network introduces two sub-networks which detect pedestrians of different scales. Outputs from the sub-networks are then combined to generate robust detection results.

Experimental results show that the proposed SA YOLOv3 framework outperforms the results of YOLOv3 on public datasets and run at an average of 11 fps on a GPU.