Current deep learning-based remote sensing object detection models rely on the powerful computing and storage re-sources of ground servers, enabling efficient processing of massive remote sensing data. However, in deployment scenarios for mobile edge devices such as Unmanned Aerial Vehicles (UAVs), the limited computational resources, storage capacity, and energy constraints make it challenging to effectively deploy large-scale models. Model compression techniques, such as lightweight design and model quantization, have become critical for enabling the practical application of remote sensing object detection algorithms. This paper proposes a wavelet time-frequency localization-based model compression framework, using the single-stage object de-tection network as the baseline: (1) By integrating depthwise separable convolution with the spatial-frequency localization charac-teristics of discrete wavelet transform, a Wavelet Depthwise Separable Convolution (W-DSConv) module is constructed to achieve lightweight model reconstruction while expanding its receptive field; (2) Leveraging wavelet frequency domain decomposition, a Wavelet Frequency-Division Quantization (W-FDQ) method for quantization-aware training is proposed, enabling independent quantization of features across different frequency bands to further compress the lightweight model. Experiments are conducted using YOLO-series models on the VisDrone2021 UAV remote sensing dataset. Results demonstrate that: (1) The W-DSConv module reduces model parameters by 45.5% and computational load by 37.4%, while limiting detection accuracy fluctuations to within 2.2%; (2) When applying 6-bit and 4-bit quantization via W-FDQ, the quantized models retain 95.8% and 92.9% of the floating-point model’s detection performance, respectively. This research provides novel technical insights for lightweight deployment of remote sensing object detection models on mobile platforms.
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