Object Detection On Pku Ddd17 Car

Metrics

mAP50

Results

Performance results of various models on this benchmark

		Paper Title
CAFR	86.7	Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection
SPNet	84.7	Specificity-preserving RGB-D Saliency Detection
DCF	83.4	Calibrated RGB-D Salient Object Detection
EFNet	83.0	Event-Based Fusion for Motion Deblurring with Cross-modal Attention
ECANet	82.2	ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
SAGate	82.0	Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
CBAM	81.9	CBAM: Convolutional Block Attention Module
FPN-Fusion	81.9	Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions
SENet	81.6	Squeeze-and-Excitation Networks
RENet	81.4	RGB-Event Fusion for Moving Object Detection in Autonomous Driving
YOLOv4	81.3	YOLOv4: Optimal Speed and Accuracy of Object Detection
CMX	80.4	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
RAMNet	79.6	Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction
SSD	73.1	SSD: Single Shot MultiBox Detector

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Object Detection On Pku Ddd17 Car | SOTA | HyperAI