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S2DB-mmWave YOLOv8n: Multi-object detection for millimeter-wave radar using YOLOv8n with optimized multi-scale

Mengqi Yuan1,2, Yajing Yuan1,3, Xiangqun Zhang1,3

  • 1School of Information Engineering, Xuchang University, Xuchang, China.

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This study introduces S2DB-mmWave YOLOv8n, a deep learning framework for millimeter-wave (mmWave) radar object detection. The enhanced model significantly improves multi-target detection accuracy and classification compared to the baseline YOLOv8n.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Radar Technology

Background:

  • Millimeter-wave (mmWave) radar offers all-weather, privacy-preserving object detection, crucial for intelligent security and transportation.
  • Existing mmWave radar object detection faces challenges in distinguishing multiple targets and algorithmic performance.
  • Deep learning approaches are vital for advancing mmWave radar capabilities.

Purpose of the Study:

  • To propose an accurate deep learning-based target detection and classification framework for mmWave radar.
  • To enhance feature extraction, detail recovery, and feature fusion in mmWave radar object detection.
  • To address limitations in multi-target discrimination and detection performance.

Main Methods:

  • Developed a novel backbone network with new convolutional layers and Simplified Spatial Pyramid Pooling - Fast (SimSPPF) module.
  • Integrated a dynamic up-sampling technique for improved fine detail recovery.
  • Incorporated a bidirectional feature pyramid network (BiFPN) for optimized feature fusion.

Main Results:

  • The S2DB-mmWave YOLOv8n model achieved 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% precision, and 90.6% recall.
  • Demonstrated significant improvements over the baseline YOLOv8n network (3.3%, 1.6%, 4.5%, and 7.7% higher, respectively).
  • Performance gains were achieved without increasing the model's parameter count.

Conclusions:

  • The proposed S2DB-mmWave YOLOv8n framework offers superior performance for mmWave radar object detection and classification.
  • The novel architectural enhancements effectively address challenges in multi-target scenarios.
  • This framework holds significant practical value for intelligent security and transportation applications.