A Improved Prior Box Generation Method for Small Object Detection

Abstract

As a task in object detection, small object detection mainly focuses on detecting objects of small size, which is more complex than general object detection. It is pivotal in various applications, e.g., small tumor detection, national defense and security, and traffic surveillance. Small objects have low pixels, few effective features, and a large influence of background noise, making small object detection extremely challenging. Currently, most object detection algorithms fail to take advantage of global context information to improve accuracy. Moreover, the conventional bounding-box proposal generation method will cause the missed detection of small targets since the target features are few and challenging to locate. To address the above problems, this paper uses the position enhancement method to improve the proposals’ generation to improve the recall rate and accuracy rate of small object detection. First, the location enhancement module adds additional keypoint location supervision to obtain target latent keypoints. Besides, we propose a keypoint expansion method to get more accurate keypoint locations. A global contextual attention mechanism is further introduced, enabling the detector to learn fine-grained features with contextual location information better. The experimental results on two datasets show that the proposed method can significantly improve the accuracy and recall of small target detection.