Live Demonstration: DVS-CIS Sensor Fusion System for Real-Time DNN-Based Object Detection

This demonstration presents a high-speed, energy-efficient sensor fusion system integrating CMOS Image Sensors (CIS) and Dynamic Vision Sensors (DVS) for advanced image recognition. Using CIS for high-res imaging and DVS for rapid event-driven capture, the FPGA-implemented architecture with an NPU running YOLOv3-Tiny achieves 18 ms inference latency with a minimal 2.78% mAP loss. Selective NPU activation based on DVS-detected regions yielded 31.5% power savings, while a custom receiver module efficiently fused DVS (13,900 fps) and CIS (60 fps) data. The system uses a power of 6.977 W on a Xilinx Zynq+ ZCU106 board.

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Published
2025-05-25
DOI
https://doi.org/10.1109/iscas56072.2025.11043578
Citations
1
Primary Topic
Infrared Target Detection Methodologies
Type
article
Field-Weighted Citation Impact
4.99

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Live Demonstration: DVS-CIS Sensor Fusion System for Real-Time DNN-Based Object Detection

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Live Demonstration: DVS-CIS Sensor Fusion System for Real-Time DNN-Based Object Detection

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article en
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Abstract

This demonstration presents a high-speed, energy-efficient sensor fusion system integrating CMOS Image Sensors (CIS) and Dynamic Vision Sensors (DVS) for advanced image recognition. Using CIS for high-res imaging and DVS for rapid event-driven capture, the FPGA-implemented architecture with an NPU running YOLOv3-Tiny achieves 18 ms inference latency with a minimal 2.78% mAP loss. Selective NPU activation based on DVS-detected regions yielded 31.5% power savings, while a custom receiver module efficiently fused DVS (13,900 fps) and CIS (60 fps) data. The system uses a power of 6.977 W on a Xilinx Zynq+ ZCU106 board.

Seoul National University (KR), Sun Moon University (KR)
Ministry of SMEs and Startups, Technology Development
Openalex Percentile: Top 6%
Infrared Target Detection Methodologies
4.99
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