ArticleSensors (Basel, Switzerland)2025
Analysis of AWS Rekognition and Azure Custom Vision Performance in Parking Sign Recognition.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Automated recognition and analysis of parking signs can greatly enhance the safety and efficiency of both autonomous vehicles and drivers seeking navigational assistance. Our study focused on identifying parking constraints from the parking signs. It offers the following novel contributions: (1) A comparative performance analysis of AWS Rekognition and Azure Custom Vision (CV), two leading services for image recognition and analysis. (2) The first AI-based approach to recognising parking signs typical for Melbourne, Australia, and extracting parking constraint information from them. We utilised 1225 images of the parking signs to evaluate the AI capabilities for analysing these constraints. Both platforms were assessed based on several criteria, including their accuracy in recognising elements of parking signs, sub-signs, and the completeness of the signs. Our experiments demonstrated that both platforms performed effectively and are close to being ready for live application on parking sign analysis. AWS Rekognition demonstrated better results for recognition of parking sign elements and sub-signs (F1 scores of 0.991 and 1.000). It also performed better in the criterion "No text missed", providing the result of 0.94. Azure CV performed better in the recognition of arrows (F1 score of 0.941). Both approaches demonstrated a similar level of performance for other criteria.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.