A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
Problem
Autonomous vehicles need reliable road damage detection and road surface classification for safe driving, and the field lacks a clear comparison of deep learning methods.
Why it matters
Undetected road damage and misclassified surfaces endanger autonomous vehicle occupants and other road users.
What the AI did
The paper reviews deep learning approaches — one-stage detectors (YOLO, SSD), two-stage detectors (Faster R-CNN) and surface-classification techniques — analyzing trade-offs between real-time processing and accuracy.
Results
not stated in source
Impact
Provides comparative insights for selecting detection methods; no adoption or measured outcomes stated.
Record
| Venue | Applied Sciences |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Adrian-Paul Botezatu, Adrian Burlacu, Ciprian Orhei |
| Citations | 32 (OpenAlex, at import time) |
| References | 101 |
| Model or method | YOLO, SSD, Faster R-CNN (reviewed) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/app14114705 |
| Open access | gold |
| Keywords | deep learning, road damage detection, surface classification, autonomous driving, object detection |
Importance rating
real but narrower advance
Road damage detection and surface classification are called critical for autonomous vehicle safety; review only, no measured outcomes, adoption, or scale stated in abstract.
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