# Research on Axle Type Recognition Technology for Under-Vehicle Panorama Images Based on Enhanced ORB and YOLOv11

## TL;DR

A new system for identifying vehicle axle types using enhanced image recognition technology improves accuracy and speed for toll management.

## Contribution

A portable system combining enhanced ORB and YOLOv11 achieves high-precision axle type recognition in under-vehicle images.

## Key findings

- The system achieves 0.98 precision and 0.99 recall in axle type recognition.
- Processing results are output within 1.5 seconds per vehicle with 99% accuracy.
- The system can be deployed in 20 minutes without road embedding.

## Abstract

What are the main findings?

Innovation of portable area array acquisition equipment: Independently developed a comprehensive panoramic image collection and identification system for vehicle chassis, which directly obtains panoramic images of the underside of vehicles through a lateral area array camera and mirror structure. The system can be deployed in 20 min without road embedding.

ORB feature matching + FeatureBooster feature enhancement + YOLOv11n image detection scheme achieves breakthrough accuracy: In the identification of vehicle axle types, the model achieved a precision of 0.98, a recall of 0.99, and an mAP@50 of 0.989 ± 0.010, demonstrating superior performance compared to traditional methods.

What is the implication of the main finding?

Addressing toll dispute pain points: Accurately distinguishing between drive axles and driven axles, providing reliable visual evidence for toll booth entrances that charge based on axle types.

Empowering real-time traffic management: The processing result for a single vehicle can be output within 1.5 s after the vehicle passes (with 99% accuracy), which can help toll booths quickly identify vehicle types and confirm charging standards on-site.

With the strict requirements of national policies on truck dimensions, axle loads, and weight limits, along with the implementation of tolls based on vehicle types, rapid and accurate identification of vehicle axle types has become essential for toll station management. To address the limitations of existing methods in distinguishing between drive and driven axles, complex equipment setup, and image evidence retention, this article proposes a panoramic image detection technology for vehicle chassis based on enhanced ORB and YOLOv11. A portable vehicle chassis image acquisition system, based on area array cameras, was developed for rapid on-site deployment within 20 min, eliminating the requirement for embedded installation. The FeatureBooster (FB) module was employed to optimize the ORB algorithm’s feature matching, and combined with keyframe technology to achieve high-quality panoramic image stitching. After fine-tuning the FB model on a domain-specific area scan dataset, the number of feature matches increased to 151 ± 18, substantially outperforming both the pre-trained FB model and the baseline ORB. Experimental results on axle type recognition using the YOLOv11 algorithm combined with ORB and FB features demonstrated that the integrated approach achieved superior performance. On the overall test set, the model attained an mAP@50 of 0.989 and an mAP@50:95 of 0.780, along with a precision (P) of 0.98 and a recall (R) of 0.99. In nighttime scenarios, it maintained an mAP@50 of 0.977 and an mAP@50:95 of 0.743, with precision and recall both consistently at 0.98 and 0.99, respectively. The field verification shows that the real-time and accuracy of the system can provide technical support for the axle type recognition of toll stations.