A Multi-stage Network for Improving the Sample Quality in Aerial Image Object Detection

Wei Han, Ruyi Feng, Lizhe Wang, Fengpeng Li, Lin Wu

Abstract: Focusing on the problems of insufficient high-quality training samples to conduct an ideal detector for high-resolution remote sensing (HRRS) image object, we applied a multi-stage based detector to apply a resampling progressively strategy, which guarantees the amount of the positive training set and minimizing overfitting. The method has a sequence of regression heads training on the samples chosen by different Intersection over Union (IoU) thresholds. The first head with a low IoU threshold trained by a large number of positive samples and can prepare more high-quality samples for the remaining branches. The subsequent heads with the increasing IoU thresholds would train on more abundant positive samples and to conduct an accurate detector and avoid the problem of overfitting. The proposed method reached the best mAP value and outperformed the comparison methods by about 10%. The experimental results show that our method can significantly improve detection performance and solve the problem of lacking high-quality samples.