Affiliation:
1. Shaanxi Key Laboratory for Network Computing and Security Technology, Department of Computer Science and Engineering, Xi’an University of Technology, No. 5 South Jinhua Road, Xi’an 710048, China
Abstract
Remote Sensing Image processing is a traditional research field, where RSI object detection is one of the most important directions. This paper focuses on an inherent problem of multi-stage object detection frameworks: the coupling error transmitting problem. In brief, because of the coupling method between the classifier and the regressor, the traditional multi-stage Detection frameworks tend to be fallible when encountering coarse object proposals. To deal with this problem, this article proposes a novel deep learning-based multi-stage object detection framework. Specifically, a novel network head architecture with a multi-to-one coupling method is proposed to avoid the coupling error of the traditional network head architecture. Moreover, it is found that the traditional network head architecture is more efficient than the novel network architecture when encountering fine object proposals. Considering this phenomenon, a proposal-consistent cooperation mechanism between the network heads is proposed. This mechanism makes the traditional network head and the novel network head develop each other’s advantages and avoid the disadvantages. Experiments with different backbone networks on three publicly available data sets have shown the effectiveness of the proposed method since mAP is proposed as 0.7% to 12.3% on most models and data sets.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences