HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images
Convolutional Neural Network (CNN)-based Synthetic Aperture Radar (SAR) target detection eliminates manual feature engineering and improves robustness but suffers from high computational costs, hindering on-satellite deployment. To address this, we propose HE-BiDet, an ultra-lightweight Binary Neura...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/16/5/549 |
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| Summary: | Convolutional Neural Network (CNN)-based Synthetic Aperture Radar (SAR) target detection eliminates manual feature engineering and improves robustness but suffers from high computational costs, hindering on-satellite deployment. To address this, we propose HE-BiDet, an ultra-lightweight Binary Neural Network (BNN) framework co-designed with hardware acceleration. First, we develop an ultra-lightweight SAR ship detection model. Second, we design a BNN accelerator leveraging four-directions of parallelism and an on-chip data buffer with optimized addressing to feed the computing array efficiently. To accelerate post-processing, we introduce a hardware-based threshold filter to eliminate redundant anchor boxes early and a dedicated Non-Maximum Suppression (NMS) unit. Evaluated on SAR-Ship, AirSAR-Ship 2.0, and SSDD, our model achieves 91.3%, 71.0%, and 92.7% accuracy, respectively. Implemented on a Xilinx Virtex-XC7VX690T FPGA, the system achieves <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>189.3</mn></mrow></semantics></math></inline-formula> FPS, demonstrating real-time capability for spaceborne deployment. |
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| ISSN: | 2072-666X |