Guided Hybrid Quantization for Object Detection in Remote Sensing Imagery via One-to-One Self-Teaching

Deep convolutional neural networks (CNNs) have improved remote sensing image analysis, but their high computational demands may limit their deployment on low-end devices with limited resources, such as intelligent satellites and unmanned aerial vehicles. Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), an innovative idea for realizing a lightweight model through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pre-trained model in advance. Second, we put forward a hybrid quantization (HQ) module that automatically acquires the optimal bit-width by imposing a threshold constraint on the distribution distance between the center point and samples in the weight search space, aiming to retain more shallow detail information that is advantageous for small object detection. Third, to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network the ability to self-judgment. A switch control machine (SCM) builds a bridge between the student and teacher networks in the same location to help the teacher reduce wrong guidance and impart vital knowledge about objects without vast background information to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs) (<2158 G) compared with any remote sensing-based, lightweight, or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.