中圖分類號(hào)：TP183 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.256663 中文引用格式： 韓德強(qiáng)，閆釗，楊淇善. 基于FPGA的多源數(shù)據(jù)融合目標(biāo)檢測的研究與實(shí)現(xiàn)[J]. 電子技術(shù)應(yīng)用，2025，51(11)：17-24. 英文引用格式： Han Deqiang，Yan Zhao，Yang Qishan. Research and implementation of multi-source data fusion target detection based on FPGA[J]. Application of Electronic Technique，2025，51(11)：17-24.
Research and implementation of multi-source data fusion target detection based on FPGA
Han Deqiang，Yan Zhao，Yang Qishan
School of Computer Science，Beijing University of Technology
Abstract： With the rapid development of technologies such as intelligent driving and robots, conventional 2D detection algorithms cannot meet the requirements of environmental perception in these scenarios, and 3D target detection is required to obtain accurate environmental information. However, the current mainstream 3D target detection models based on multi-source data fusion rely on high-computing and high-power platforms, and are difficult to implement on low-performance embedded platforms. In response to these problems, a method for implementing multi-source fusion 3D target detection on a low-power FPGA platform is proposed. By fusing the LiDAR point cloud and camera image data, the lack of point cloud feature information is compensated to achieve higher accuracy and detection stability. At the same time, combined with the characteristics of the FPGA platform, the fused features are screened and processed, and the model is compressed in combination with a quantization strategy. After experiments, the fusion method significantly improves the accuracy of small objects, and the quantized model runs successfully on the end-side FPGA platform with an average 3D accuracy loss of less than 3%.
Key words : LiDAR；3D object detection；FPGA；embedded；multi-sensor fusion
