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

We propose a deep learning method to automatically detect personal protectiveequipment (PPE), such as helmets, surgical masks, reflective vests, boots andso on, in images of people. Typical approaches for PPE detection based on deeplearning are (i) to train an object detector for items such as those listedabove or (ii) to train a person detector and a classifier that takes thebounding boxes predicted by the detector and discriminates between peoplewearing and people not wearing the corresponding PPE items. We propose a noveland accurate approach that uses three components: a person detector, a bodypose estimator and a classifier. Our novelty consists in using the poseestimator only at training time, to improve the prediction performance of theclassifier. We modify the neural architecture of the classifier by adding aspatial attention mechanism, which is trained using supervision signal from thepose estimator. In this way, the classifier learns to focus on PPE items, usingknowledge from the pose estimator with almost no computational overhead duringinference.

SuPEr-SAM: Using the Supervision Signal from a Pose Estimator to Train a Spatial Attention Module for Personal Protective Equipment Recognition