Simplifying Two-Stage Object Detectors for On-Board Remote Sensing

Jaemin Kang, Hoeseok Yang, Hyungshin Kim

Abstract: Deep learning has been applied to object detection in remotely sensed images. Typically, remote sensing object detection is performed on the ground rather than on-board due to the limited computational resources available on embedded systems. This offloading introduces delays in acquiring mission-critical information, restricting its applicability to real-time scenarios. To enable on-device object detection, research has focused on designing efficient detectors and applying model compression techniques to reduce inference latency. Nonetheless, there is an absence of relevant research that maintains the accuracy of remote sensing detectors. Currently, the majority of remote sensing detectors operate as two-stage detectors. Therefore, there is a need for efficient research into two-stage detectors. In this paper, we propose a model simplification method for two-stage object detectors. Instead of utilizing a conventional feature pyramid network (FPN), our approach employs a single feature extraction process within the two-stage detector. However, relying solely on a single feature degrades detection accuracy. To compensate for the accuracy drop, we modify the method of selecting positive anchors used in training. We apply a method to determine the threshold for positive anchors based on the intersection over union (IoU), with the k anchors closest to the object. To further enhance accuracy, we apply a high-pass filter to increase the objectness score of hard-to-detect objects on the region proposal network (RPN) score map. The proposed method is compatible with any two-stage detector that integrates an FPN. Experiments conducted on state-of-the-art two-stage detectors, such as Oriented-RCNN and LSKNet, demonstrate that our method reduced computation costs up to 58.1% while achieving a 1.6%p increase in average accuracy on the DOTAv2.0 dataset. With the fewer RoIs, our approach in the Oriented-RCNN achieves a 31.8% improvement in throughput while maintaining the same average accuracy.

External IDs:doi:10.1109/access.2025.3599526