Vision Mamba-based DETR with tailored FPN and token pruning achieves a better performance-efficiency balance than RT-DETR for maritime object detection.
Ssd: Single shot multibox detector
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5verdicts
UNVERDICTED 5representative citing papers
AMIEOD combines a multi-expert enhancement module with detection-guided regression and selection losses to raise object detection accuracy in low-illumination images.
A modular system fuses object detection, segmentation, and LiDAR-improved depth estimation to achieve 0.63 m MAE for obstacle distances on synthetic railway data.
A survey organizes synthetic data use, digital twin simulation, and domain adaptation techniques for autonomous driving while identifying open challenges like Sim2Real transfer.
A lightweight multi-task neural network enables real-time driver state monitoring on embedded systems by predicting multiple face indicators in one forward pass.
citing papers explorer
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Increasing the Efficiency of DETR for Maritime High-Resolution Images
Vision Mamba-based DETR with tailored FPN and token pruning achieves a better performance-efficiency balance than RT-DETR for maritime object detection.
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AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes
AMIEOD combines a multi-expert enhancement module with detection-guided regression and selection losses to raise object detection accuracy in low-illumination images.
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Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments
A modular system fuses object detection, segmentation, and LiDAR-improved depth estimation to achieve 0.63 m MAE for obstacle distances on synthetic railway data.
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From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving
A survey organizes synthetic data use, digital twin simulation, and domain adaptation techniques for autonomous driving while identifying open challenges like Sim2Real transfer.
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Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network
A lightweight multi-task neural network enables real-time driver state monitoring on embedded systems by predicting multiple face indicators in one forward pass.