Scale-Adaptive Tiny Object Detection Enhanced by Across-Scale and Shape-Preserved Semantic Location

In tiny object detection, the main challenges are tiny objects’ weak feature responses and possible semantic disappearance in deep networks. To address the problems, we proposed an Instance-level, Scale-adaptive, Shape-preserved, and Semantic-consistent Supervision (I4S) module for better locating tiny objects. It models across-scale feature responses of an instance as an elliptic cone, whose axis indicates the instance’s semantic center in different scales. By the cone supervision on across-scale feature maps, it not only exploits the classification semantic from multi-scale feature maps, but also preserves and explores the information of instance shape in consecutive scales. Experiment results on public datasets proved that our method can effectively improve the location accuracy and significantly reduce the missed detection rate comparing with the method of directly fusing multi-scale features.