Object Detection in High-Resolution Remote Sensing Images Based on a Hard-Example-Mining Network

In recent years, object detection in remote sensing images (RSIs) has attracted much attention for its application value. Compared with traditional methods that are based on manually extracted features, deep learning methods have a great advantage for object detection and have been vastly promoted. However, existing deep learning methods leave much to be desired in the field of RSI object detection due to the large-scale range of the objects and the complex image backgrounds in RSIs. Algorithms need to be specially optimized for this situation. To solve this problem, we propose an effective deep learning-based RSI object detection framework called the multiscale hard-example-mining network (MSHEMN), which is composed of three parts. First, we use the existing ResNet-50 for feature extraction. Second, we propose a multiscale region proposal network (MSRPN), which improves the existing top–down pathway feature pyramid architecture of feature pyramid network (FPN) by adding lateral connection block (LCB) and adaptive feature merge (AFM) to extract features that combine high-resolution and strong semantical information. Finally, a hard-example-mining network (HEMN), which is a cascade multistage detection network integrated with a hard example mining strategy, is proposed to make the detection network focus on hard examples during the training phase by changing the input data distribution of each stage. Extensive experiments on the High-Resolution Remote Sensing Detection (HRRSD) data set have shown the effectiveness of our proposed method, which achieves an average precision (AP) of 62.6 on the testing data set.