Work

HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images

Year: 2025

Type: article

Abstract: 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 189.3 FPS, demonstrating real-time capability for spaceborne deployment.

Source: Micromachines

DOI: 10.3390/mi16050549

Authors: Dezheng Zhang, Zehan Liang, Rui Cen, Zhihong Yan, Rui Wan, Dong Wang

Institution: Beijing Jiaotong University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,534

Funder: Natural Science Foundation of Beijing Municipality