ArticlePloS one2026
A machine-vision framework for electric vehicle charging hole localization based on contour-driven detection and geometric verification.
Article in PloS one, 2026. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate localization of charging holes is an indispensable perception step for the automatic charging system of electric vehicles, since the success of subsequent charging-gun insertion depends directly on the precise estimation of the geometric centers of the holes on the vehicle charging interface. This paper presents a lightweight machine-vision framework for charging hole localization based on contour-driven detection and geometric verification. The proposed framework integrates image filtering, RGB-to-HSL color-space conversion, parameterized HSL adjustment, two-stage contour detection, minimum enclosing circle fitting, and Hough circle verification into a deterministic pipeline. The framework is interpretable, does not require a learning-based training process, and can be deployed on conventional CPU-based computing resources. The image data used in this study were acquired by a monocular RGB camera mounted on the charging robot end-effector, in the same sensing configuration in which the system is intended to operate. A total of 80 images were collected from six vehicles equipped with the same five-hole charging interface, under realistic variations of illumination, viewing angle, reflective interference, and background complexity. Detailed parameter selection rationale, a parameter perturbation analysis, and a condition-specific ablation analysis are reported. On an Intel Core i7-12700 CPU with 16 GB RAM and no GPU acceleration, the proposed framework achieves an overall mean localization error of 4.2 pixels and a 96.25% detection success rate, with an average per-image processing time of approximately 60 ms. The results demonstrate that the proposed framework provides accurate, robust, and engineering-deployable charging-hole localization for the target charging interface.
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.