Detection of Road-Surface Anomalies Using a Smartphone Camera and Accelerometer.

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.

Classification and Segmentation of Longitudinal Road Marking using Convolutional Neural Networks for Dynamic Retroreflection Estimation.

Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated.

Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning.

The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages.

Comparison of sirolimus-eluting stent and paclitaxel-eluting stent for long-term cardiac adverse events in diabetic patients: the Korean Multicenter Angioplasty Team (KOMATE) Registry.

BACKGROUND: There is some controversy on long-term cardiac outcomes between sirolimus-eluting stents (SES) and paclitaxel-eluting stents (PES) in diabetes mellitus (DM). We compared cardiac adverse events after SES and PES implantation in patients with DM over a period of 3 year. METHODS: A total of 634 patients with DM treated with SES (n = 428) or PES (n = 206) were consecutively enrolled in the KOMATE registry from 2003 to 2004. We assessed major adverse cardiac events (MACEs, cardiovascular death, nonfatal myocardial infarction, ischemia driven target vessel revascularization) and stent thrombosis (ST) according to the definitions set by the Academic Research Consortium. RESULTS: Propensity score (PS) analysis was performed to adjust different baseline characteristics. The mean follow-up duration was 38 +/- 8 month (at least 36 month and up to 53 month). The 3-year MACE rate did not show a significant difference between the two groups [52 (12.1%) in SES vs. 29 (14.1%) in PES, P = 0.496]. The definite and probable ST at 3 year were similar in both SES and PES [12 (2.8%) in SES vs. 7 (3.4%) in PES, P = 0.681]. There were no differences in hazard ratio for MACE and ST between two stents [MACE, crude: 0.844 (0.536-1.330) and adjusted for PS: 0.858 (0.530-1.389); ST, crude: 0.820 (0.323-2.083) and adjusted for PS: 0.960 (0.357-2.587)]. CONCLUSIONS: The present study demonstrated that long-tem cardiac outcomes including ST were not significantly different between SES and PES in patients with DM.

Sirolimus- versus paclitaxel-eluting stent implantation for unprotected left main coronary artery stenosis.

We performed this study in order to compare the immediate and mid-term outcomes of sirolimus-eluting stents (SES) and paclitaxel-eluting stents (PES) in lesions of the unprotected left main coronary artery (LMCA). We assessed 54 patients from 5 centers who had undergone unprotected LMCA stenting (35 SES and 19 PES). The procedural success rates were 100 and 95%, respectively, in the SES and PES patients (p = 0.19). At the 6-month clinical follow-up, the event-free probability was 100% in the SES group, and 88% in the PES group (p = 0.07). At the 6-month angiographic follow-up (n = 24), the SES group exhibited a slightly lower late loss than did the PES group (0.24 +/- 0.44 vs. 0.65 +/- 0.60 mm, p = 0.09), and the restenosis rates were 8 and 9% (p = 0.94) in the SES and PES patients, respectively. In conclusion, both groups exhibited excellent in-hospital and 6-month outcomes with no significant differences between them.