This study presents a truck classification method using panoramic side-view images to meet the Ministry of Land, Infrastructure and Transport’s 12-category standard (types 4–12). The system captures a vehicle’s full side profile via a panoramic imaging device, ensuring complete wheel visibility. A YOLOv12-based deep learning model detects wheels, and image processing extracts their center coordinates. Pixel distances between adjacent wheels are calculated and normalized to determine axle spacing patterns, which, together with wheel count, are applied to a rule-based classifier. Tests on 1,200 real-world panoramic truck images (1,000 for training, 200 for testing) achieved a mean average precision of 96.1% for wheel detection and 90.5% overall classification accuracy. The method offers explainable classification through measurable structural features, supporting applications in smart tolling, road usage billing, overloading enforcement, and autonomous vehicle perception.

Overloaded and improperly loaded trucks cause serious road hazards, such as rollovers and cargo falls. Although automatic enforcement methods are being studied, they face challenges in accuracy and legal application. Thus, a technology for direct tracking and enforcement is needed. This study uses EfficientNet to extract features of vehicles and license plates, and applies cosine similarity to identify the same vehicle. Comparisons were divided into “same vehicle” and “similar vehicle,” with a threshold-based method and five classification types. Results showed that the average similarity of the same vehicle group was 0.11 higher than that of the similar vehicle group. The accuracy of correctly identifying the same vehicle was 84.54%. Integrating OCR or LPR is expected to further improve tracking performance.

The lane designation and the bus-only lane system for traffic speed and road safety are difficult to crack down on, and for this purpose, crackdown methods using image recognition technologies are being studied. Existing studies require continuous learning or additional equipment, and it is difficult to classify combined vehicles such as vans and pickup trucks. Therefore, in this study, YOLO and EasyOCR were mixed to classify combined vehicles through vehicle type symbols. For combined vehicles, higher accuracy was shown than classification using YOLO. Due to the nature of Hangul, the accuracy was slightly lowered because the OCR was not accurately recognized, but if it is used with the existing YOLO classification, high accuracy of crackdown will be possible.

We investigate two abnormal CME-Storm pairs that occurred on 2014 September 10 - 12 and 2015 March 15 - 17, respectively. The first one was a moderate geomagnetic storm (Dstmin  -75 nT) driven by the X1.6 high speed flare-associated CME (1267 km s−1) in AR 12158 (N14E02) near solar disk center. The other was a very intense geomagnetic storm (Dstmin  -223 nT) caused by a CME with moderate speed (719 km s−1) and associated with a filament eruption accompanied by a weak flare (C9.1) in AR 12297 (S17W38). Both CMEs have large direction parameters facing the Earth and southward magnetic field orientation in their solar source region. In this study, we inspect the structure of Interplanetary Flux Ropes (IFRs) at the Earth estimated by using the torus fitting technique assuming self-similar expansion. As results, we find that the moderate storm on 2014 September 12 was caused by small-scale southward magnetic fields in the sheath region ahead of the IFR. The Earth traversed the portion of the IFR where only the northward fields are observed. Meanwhile, in case of the 2015 March 17 storm, our IFR analysis revealed that the Earth passed the very portion where only the southward magnetic fields are observed throughout the passage. The resultant southward magnetic field with long- duration is the main cause of the intense storm. We suggest that 3D magnetic field geometry of an IFR at the IFR-Earth encounter is important and the strength of a geomagnetic storm is strongly affected by the relative location of the Earth with respect to the IFR structure.