Abstract
The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.
Highlights
The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs)
Many researchers have devoted themselves to the research of object detection in RSIs based on deep learning and achieved good results [8,9,10,11]
To tackle the above problems, we propose an end-to-end detection framework named the attention-guided dense network (ADNet), which is based on Faster R-convolutional neural networks (CNNs)
Summary
Sensing Images Based on Attention-Guided Dense Network. ISPRS Int. Many researchers have devoted themselves to the research of object detection in RSIs based on deep learning and achieved good results [8,9,10,11] Most of these methods are designed for single objects with regular geometric appearance and structure such as ships, vehicles, and airplanes. According to the research mentioned above, existing studies mostly focus on large composite objects which are in large remote sensing scenes These methods have not considered composite objects like primary and secondary schools (PSSs), which have various appearances in different scales and regions. With the rapid development of remote sensing technology, a large number of high-resolution RSIs are obtained, which contain abundant spatial information, clear and detailed textural features, and topological relationships. Guided by the attentive results, the dense feature fusion structure can obtain hierarchical feature representation with enhanced discriminative ability and precisely detect objects at different scales and sizes. The main contributions of our work are summarized as follows: The main contributions of ourdetection work areframework summarized as follows: We propose an end-to-end called
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