Efficient Detection in Aerial Images for Resource-Limited Satellites

Object detection in aerial images is a challenging task because of the complex background and various orientations of the objects. Currently, many detectors have made significant progress in improving mAP scores, but they do not achieve the improvements in efficiency and model size. In this letter, we propose a detector for resource-limited satellite network, namely, the simple convolutional neural networks (simple-CNNs), which can be directly applied in actual application scenarios using small sample data. Specifically, a new sample 16-layer network is devised to reduce the model size. Meanwhile, we calculate a new anchor for ten classes and carefully design a change-IOU Loss (CI-Loss) function for horizontal bounding box detection to improve the accuracy. Extensive experiments on two remote sensing public data sets (NWPU VHR-10 and part of DIOR) show good performance for the efficiency of our detector. In particular, our simple-CNN achieves 68.9 mAP on the NWPU VHR-10 test-dev data set with 90 M parameters and a test speed of 72 ms, and it also has good performance on a part of DIOR.