On the public citation graph
Deep Learning-Based Pedestrian Detection in Autonomous Vehicles: Substantial Issues and Challenges
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1 of the 105 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1007/s00500-020-04999-1 (notice 2024-08-12).
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.cej.2021.130388openalex-snapshotImprovement of air cathode performance in microbial fuel cells by using catalysts made by binding metal-organic framework and activated carbon through ultrasonication and solution precipitation
inferredcites →https://doi.org/10.3390/app10030809openalex-snapshotPedestrian Detection at Night in Infrared Images Using an Attention-Guided Encoder-Decoder Convolutional Neural Network
inferredcites →https://doi.org/10.3390/rs13020200openalex-snapshotCascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function
inferredcites →https://doi.org/10.3390/rs13132538openalex-snapshotInfrared and Visible Image Object Detection via Focused Feature Enhancement and Cascaded Semantic Extension
inferredcites →https://doi.org/10.3390/s17010018openalex-snapshotA Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data
inferredcites →https://doi.org/10.3390/s22187014openalex-snapshotComparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s11760-023-02681-1openalex-snapshotMachine learning-driven pedestrian detection and classification for electric vehicles: integrating Bayesian component network analysis and reinforcement region-based convolutional neural networks
inferred← citeshttps://doi.org/10.1063/5.0234982openalex-snapshotSmart cars and autonomous driving in public transportation: An analysis of European and Greek perspectives
inferred← citeshttps://doi.org/10.1080/20964471.2026.2649432openalex-snapshotContext-aware landmark recognition in location-based augmented reality: A switching framework with a MobileNet backbone for cultural heritage engagement
inferred← citeshttps://doi.org/10.1109/access.2025.3574785openalex-snapshotBenchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System
inferred← citeshttps://doi.org/10.1117/12.3013514openalex-snapshotTraffic light recognition and V2I communications of an autonomous vehicle with the traffic light for effective intersection navigation using YOLOv8 and MAVS simulation
inferred← citeshttps://doi.org/10.3390/app122211364openalex-snapshotApplication of Machine Learning Techniques for Predicting Potential Vehicle-to-Pedestrian Collisions in Virtual Reality Scenarios
inferred← citeshttps://doi.org/10.3390/app13063995openalex-snapshotTarget Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis
inferred← citeshttps://doi.org/10.3390/e25071022openalex-snapshotContour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection
inferred← citeshttps://doi.org/10.3390/futuretransp3010012openalex-snapshotA Secure Traffic Police Remote Sensing Approach via a Deep Learning-Based Low-Altitude Vehicle Speed Detector through UAVs in Smart Cites: Algorithm, Implementation and Evaluation
inferred← citeshttps://doi.org/10.3390/make5030041openalex-snapshotDeep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review
inferred← citeshttps://doi.org/10.3390/s23177361openalex-snapshotHolistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions
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