We are studying in-orbit real-time object detection for remote sensing satellites. Due to the small object size of remote sensing images, it is hard to achieve high detection accuracy, especially for resource-constrained spacecraft computers. Lightweight object detection models such as YOLO and SSD are feasible choices to achieve acceptable detection speed on board. This study proposes an accuracy-improvement method for the lightweight neural networks with an upscaling ratio estimator without retraining the model. The estimator exploits a scaling ratio that determines how much the image should be resized. With our scaling estimator, we have achieved 10.09% higher accuracy than the original YOLOv4-Tiny models with a 40% detection speed overhead.
Object detection from remote sensing images has been performed on the ground. Recently, on-board object detection has been studied only to show its feasibility with single-stage detectors. However, highly accurate models such as two stage detectors are compute intensive so that they are too slow to run on power-constrained on-board computers. In this paper, we propose a speed-up method for two-stage detectors. Two-stage detectors extract features and ROIs(Region of Interest) in the first stage and then classify them at the second stage. This structure gives high accuracy but induces large inference latency. In remote sensing images from satellites, object size is small relative to the whole image. Based on this characteristic, we propose to exclude features related to the large objects in the first stage. To verify our concept, we have selected various R-CNN models as two-stage object detectors. We have implemented our methods on two NVIDIA Jetson boards. We have achieved 1.8x speed up in inference latency with 5% accuracy drop with the small object dataset.
This paper addresses a novel robust watermarking method for digital images using local invariant features. Most previous watermarking algorithms are unable to resist geometric distortions that desynchronize the location where copyright information is inserted. We propose a watermarking method that is robust to geometric distortions. In order to resist geometric distortions, we use a local invariant feature of the image called the scale-invariant feature transform (SIFT), which is invariant to translation and scaling distortions. The watermark is inserted into the circular patches generated by the SIFT. Rotation invariance is achieved using the translation property of the polar-mapped circular patches. Our method belongs to the blind watermark, because we do not need the original image during detection. We have performed an intensive simulation to show the robustness of the proposed method. The simulation results support the contention that our method is robust against geometric distortion attacks as well as signal-processing attacks. We have compared our results with those of other methods, and our method outperforms them.
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