Multi-Vision Network for Accurate and Real-Time Small Object Detection in Optical Remote Sensing Images
Wenxuan Han, Alifu Kuerban, Yuchun Yang, Zitong Huang, Binghui Liu, Jie Gao
Abstract
Accurate and real-time detection of airplanes, cars, and ships in remote sensing images is an important but challenging task that plays an important role in both military and civilian life. The most challenging issues posed by this task are the intensive and tiny size of objects and the complexity of application scenarios. In this letter, we propose a multi-vision small object detector that can rapidly and accurately detect airplanes, cars, and ships in remote sensing images. We make the following three contributions: a multiscale residual block (MRB) is proposed, whereby dilated convolution is employed in a cascade residual block to capture multiscale context information, thus improving the feature representation ability of convolutional neural networks; a multiscale receptive field enhancement module (MRFEM) is proposed that combines features obtained using dilated convolution at different dilation rates to further enhance the multiscale feature representation of the remote sensing targets; and a multi-vision network (MVNet) is presented that uses multiple low-level feature maps with multi-branch convolution to detect small objects. Experimental results show that the proposed method can achieve a significant mean average precision (mAP) of 94.70% in remote sensing images and can run at 24 FPS on a single NVIDIA 1080Ti GPU.