Bridge-over-water detection via modulated deformable convolution and attention mechanisms

Abstract

Bridge-over-water detection plays vital role in urban surveillance and military reconnaissance. Bridges have arbitrary orientations and extreme aspect ratios in remote sensing images, and the preceding works cannot adequately extract bridge-related features. Small bridges are difficult to detect accurately in optical remote sensing images. The oriented bounding box annotations are required by previous deep-learning-based methods for detecting rotated objects. But obtaining the annotations is a laborious task. Though widely studied previously, they are still challenging problems. To address these problems, modulated deformable convolution and attention mechanisms were introduced in this paper. Modulated deformable convolution made the receptive field more flexible. The feature extraction capability of the network was enhanced. A new weighted structure was designed to quantify the contributions of channel and spatial attention mechanisms. A selective attention usage strategy was proposed to improve the detection performance. To locate bridge-over-water more precisely, a new bounding box conversion module was presented. There was no need for oriented bounding box annotations, and the process only relied on bridge-related prior knowledge. Multiple experiments were performed to verify the effectiveness of proposed methods.