The framework, presented by Wei Luo, Qiantao Huo, and Xin Li, 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 single deterministic pipeline. Because the method is rule-based rather than learned, the authors describe it as interpretable and deployable on conventional CPU-based computing resources, avoiding the training data requirements and opaque decision-making associated with neural-network approaches.

The image data were acquired by a monocular RGB camera mounted on the charging robot end-effector, the same sensing configuration in which the system is intended to operate. In total, 80 images were collected from six vehicles equipped with the same five-hole charging interface, capturing realistic variations in illumination, viewing angle, reflective interference, and background complexity.

Performance was measured on an Intel Core i7-12700 CPU with 16 GB of RAM and no GPU acceleration. Under these conditions, the framework achieved the reported 4.2-pixel mean localization error and 96.25% detection success rate, with an average per-image processing time of approximately 60 milliseconds. The authors also report detailed parameter selection rationale, a parameter perturbation analysis, and a condition-specific ablation analysis examining how the pipeline behaves across the tested imaging conditions.

According to the study, the results demonstrate that the proposed framework provides accurate, robust, and engineering-deployable charging-hole localization for the target five-hole charging interface. The combination of sub-100-millisecond processing on commodity CPU hardware and a fully interpretable pipeline positions the approach as a practical perception component for automatic electric vehicle charging robots operating in the lighting and reflection conditions tested in the study.

Subject of Research: Machine-vision localization of electric vehicle charging holes for automatic charging systems

Article Title: A machine-vision framework for electric vehicle charging hole localization based on contour-driven detection and geometric verification

Article References: Luo, W., Huo, Q., & Li, X. (2026). A machine-vision framework for electric vehicle charging hole localization based on contour-driven detection and geometric verification. PLOS One, 21(10), e0359570. https://doi.org/10.1371/journal.pone.0359570

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0359570

Keywords: electric vehicles, machine vision, charging hole localization, contour detection, Hough circle transform, HSL color space, charging robots, computer vision, geometric verification, CPU deployment, autonomous charging, machine-vision