A Lightweight Robot-View Visual Sensing Framework for CPU-Oriented License Plate Detection and Recognition in Mobile Robotic Scenarios

Mobile inspection robots require reliable license plate detection and recognition under constrained computing resources, small-scale or distant imaging conditions, motion blur, and complex background interference. To address these coupled challenges, this paper proposes a lightweight robot-view visual sensing framework for CPU-oriented license plate perception. Instead of simply stacking network modules, the proposed framework follows a unified design principle of reducing redundant computation while compensating for task-critical visual information. In the detection stage, a YOLOv8-MGL detector is developed based on YOLOv8n by combining GhostC2f-based lightweight feature aggregation with LSKAlite-based contextual enhancement after the SPPF module. In the recognition stage, SimAM is embedded into LPRNet to enhance discriminative character responses under motion blur, low resolution, and local degradation without introducing additional learnable parameters. Experiments on the held-out EDRV-LP test set show that YOLOv8-MGL achieves 99.5% mAP50 and 71.1% mAP50:95, while reducing the number of parameters from 3.01 M to 2.77 M and GFLOPs from 8.1 to 7.5 compared with YOLOv8n. On a CPU-only Intel Xeon Platinum 8260C platform, YOLOv8-MGL achieves 23.98 FPS. SimAM-LPRNet improves the module-level cropped-plate recognition accuracy from 83.10% to 87.17%. To further examine system-level feasibility, a supplementary YOLOv8-MGL + CRNN-CTC pipeline is evaluated from raw images to final plate strings, achieving 91.0% exact recognition accuracy on the held-out EDRV-LP test set, 92.0% on a non-overlapping external CCPD subset, and 13.25 FPS for complete CPU-only processing. These results demonstrate that the proposed framework provides a favorable trade-off among model compactness, localization quality, recognition robustness, and CPU-oriented inference feasibility for mobile robotic inspection scenarios.