Von Mises ensemble yields angular uncertainty estimates that integrate directly into tracking via closed-form likelihoods and shows stronger perturbation sensitivity than evidential deep learning on radar DOA tasks.
Cbam: Convolutional block attention module
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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An enhanced YOLOv8 model with Ghost Module, CBAM, and DCNv2 achieves 95.4% mAP@0.5 on the KITTI dataset for vehicle detection, an 8.97% gain over the baseline.
citing papers explorer
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Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets
Von Mises ensemble yields angular uncertainty estimates that integrate directly into tracking via closed-form likelihoods and shows stronger perturbation sensitivity than evidential deep learning on radar DOA tasks.
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Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems
An enhanced YOLOv8 model with Ghost Module, CBAM, and DCNv2 achieves 95.4% mAP@0.5 on the KITTI dataset for vehicle detection, an 8.97% gain over the baseline.