Spatial Cognition-Driven Deep Learning for Car Detection in Unmanned Aerial Vehicle Imagery

Abstract

Small object detection is the main challenge for image detection of unmanned aerial vehicles (UAVs), especially with small pixel ratios and blurred boundaries. In this article, a one-stage detector (SF-SSD) is proposed with a new spatial cognition algorithm. The deconvolution operation is introduced to a feature fusion module, which enhances the representation of shallow features. These more representative features prove effective for small-scale object detection. Empowered by a spatial cognition method, the deep model can redetect objects with less-reliable confidence scores. This enables the detector to improve detection accuracy significantly. Both between-class similarity and within-class similarity are fully exploited to suppress useless background information. This motivates the proposed model to take full use of semantic features in the detection process of multiclass small objects. A simplified network structure can improve the speed of object detection. The experiments are conducted on a newly collected dataset (SY-UAV) and the benchmark datasets (CARPK and PUCPR+). To further demonstrate the effectiveness of the spatial cognition module, a multiclass object detection experiment is conducted on the Stanford Drone dataset (SDD). The results show that the proposed model achieves high frame rates and better detection accuracies than the state-of-the-art methods, which are 90.1% (CAPPK), 90.8% (PUCPR+), and 91.2% (SDD).