A Method of Fusing Probability-Form Knowledge into Object Detection in Remote Sensing Images

In recent years, dramatic progress in object detection in remote sensing images has been made due to the rapid development of convolutional neural networks (CNNs). However, most existing methods solely pay attention to training a suitable network model to extract more powerful features in order to solve the problem of false detections and missed detections caused by background complexity, various scales, and the appearance of the object. To open up new paths, we consider embedding knowledge into geospatial object detection. As a result, we put forward a method of digitizing knowledge and embedding knowledge into detection. Specifically, we first analyze the training set and then transform the probability into a knowledge factor according to an analysis using an improved version of the method used in existing work. With a knowledge matrix consisting of knowledge factors, the Knowledge Inference Module (KIM) optimizes the classification in which the residual structure is introduced to avoid performance degradation. Extensive experiments are conducted on two public remote sensing image data sets, namely DOTA and DIOR. The experimental results prove that the proposed method is able to reduce some false detections and missed detections and obtains a higher mean average precision (mAP) performance than the baseline method.