A Theoretical Framework Towards Building a Lightweight Model for Pothole Detection using Knowledge Distillation Approach

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Veröffentlicht in:SHS Web of Conferences Jg. 139; S. 03002
Hauptverfasser: Ramli Musa, Mohamed Hamada, Mohammed Hassan
Format: Journal Article
Sprache:Japanisch
Veröffentlicht: EDP Sciences 01.01.2022
Schlagworte:
Anomaly detection
Anomaly Detection in High-Dimensional Data
Artificial intelligence
Automated Pavement Inspection and Maintenance
Civil and Structural Engineering
Cloud computing
Computer science
Computer security
Computer Vision and Pattern Recognition
Deep Learning
Deep Learning in Computer Vision and Image Recognition
Defect Detection
Edge device
Engineering
Enhanced Data Rates for GSM Evolution
FOS: Earth and related environmental sciences
Geology
H
knowledge distillation
model compression
Object detection
Operating system
Outlier Detection
Pattern recognition (psychology)
Petrology
Physical Sciences
Pothole (geology)
pothole detection
SAFER
Simulation
Social Sciences
ISSN:2261-2424
Online-Zugang:Volltext
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ISSN:2261-2424
DOI:10.60692/g63pe-bxy22