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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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2072-666X/16/5/549 |
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| author | Dezheng Zhang Zehan Liang Rui Cen Zhihong Yan Rui Wan Dong Wang |
| author_facet | Dezheng Zhang Zehan Liang Rui Cen Zhihong Yan Rui Wan Dong Wang |
| author_sort | Dezheng Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c55d5f6bf0ae41b2867147f8ccacc547 |
| institution | OA Journals |
| issn | 2072-666X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj-art-c55d5f6bf0ae41b2867147f8ccacc5472025-08-20T01:56:38ZengMDPI AGMicromachines2072-666X2025-04-0116554910.3390/mi16050549HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR ImagesDezheng Zhang0Zehan Liang1Rui Cen2Zhihong Yan3Rui Wan4Dong Wang5Institute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaConvolutional 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.https://www.mdpi.com/2072-666X/16/5/549binary neural networksship detectionsynthetic aperture radar (SAR)field programmable gate array (FPGA) |
| spellingShingle | Dezheng Zhang Zehan Liang Rui Cen Zhihong Yan Rui Wan Dong Wang HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images Micromachines binary neural networks ship detection synthetic aperture radar (SAR) field programmable gate array (FPGA) |
| title | HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images |
| title_full | HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images |
| title_fullStr | HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images |
| title_full_unstemmed | HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images |
| title_short | HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images |
| title_sort | he bidet a hardware efficient binary neural network accelerator for object detection in sar images |
| topic | binary neural networks ship detection synthetic aperture radar (SAR) field programmable gate array (FPGA) |
| url | https://www.mdpi.com/2072-666X/16/5/549 |
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