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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Cycling is an increasingly popular method of transportation forsustainability and health benefits. However, cyclists face growing risks,especially when encountering semi-trailer trucks. This study aims to reduce thenumber of truck-cyclist collisions, which are often caused by semi-trailertrucks making right-hook turns and poor driver attention to blind spots. Toachieve this, we designed a visual-based blind spot warning system that candetect cyclists for semi-trailer truck drivers using deep learning. First,several greater than 90% mAP cyclist detection models, such as the EfficientDetLite 1 and SSD MobileNetV2, were created using state-of-the-art lightweightdeep learning architectures fine-tuned on a newly proposed cyclist imagedataset composed of a diverse set of over 20,000 images. Next, the objectdetection model was deployed onto a Google Coral Dev Board mini-computer with acamera module and analyzed for speed, reaching inference times as low as 15milliseconds. Lastly, the end-to-end blind spot cyclist detection device wastested in real-time to model traffic scenarios and analyzed further forperformance and feasibility. We concluded that this portable blind spot alertdevice can accurately and quickly detect cyclists and have the potential tosignificantly improve cyclist safety. Future studies could determine thefeasibility of the proposed device in the trucking industry and improvements tocyclist safety over time.

A semi-trailer truck right-hook turn blind spot alert system for detecting vulnerable road users using transfer learning