English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Increasingly more governmental organizations in the U.S. have started to implement artificial intelligence to enhance the asset management process with an objective of controlling the costs of data collection. To help the Wyoming Department of Transportation (WYDOT) to automate the data collections process, related to various assets in the state, an automated assets management data collection was proposed. As an example, the automated traffic barriers asset dataset would collect geometric characteristics, and barriers’ materials’ conditions, e.g., being rusty or not. The information would be stored and accessed for asset-management-decision-making and optimization process to fulfill various objectives such as traffic safety improvement, or assets’ enhancement. For instance, the State of Wyoming has more than a million feet of roadside barriers, worth more than 100 million dollars. One-time collection of various characteristics of those barriers has cost the state more than half a million dollars. Thus, this study, as a first step for comprehensive data collection, proposed a novel approach in identification of roadside barrier types. Pre-trained inception v3, denseNet 121, and VGG 19 were implemented in this study. Transfer learning was used as there were only 250 images for training of the dataset for each category. For that method, the topmost layers were removed, along with adding two more new layers while freezing the remaining layers. This study achieved an accuracy of 97% by the VGG 19 network, training only the few last layers of the model along with adding two dense layers for top layers. The results indicated that although there are not enough observations related to traffic barrier images, a transfer learning application could be considered in data collection. A simple architecture non-transfer model was also implemented. This model achieved an accuracy of 85%, being better that the two other transfer learning techniques. It should be reiterated that although non-transfer learning technique outperformed inception and denseNet networks, it comes short significantly when it come to the VGG network.

Convolutional Neural Network for Roadside Barriers Detection: Transfer Learning versus Non-Transfer Learning