Protecting the personal safety of on-site workers is an important task in enterprise production. In order to achieve widespread deployment to edge computing terminals, a lightweight object detection algorithm based on YOLOv5 is used to implement the personal safety detection task for workers. To achieve a lightweight task, PConv is utilized as the convolutional layer to decrease computational complexity, while Bi-Level Routing Attention is incorporated to enhance model accuracy. Furthermore, four detection heads are employed to improve object recognition capabilities. After experimentation, the precision can be improved by 3.4% compared with the baseline model, the parameters are reduced by 1.91MB, and the model size is decreased by 3.2MB.
Conventional road boundary intelligent identification methods mainly use black-and-white optical driving method to generate road boundary detection binary image, which is easily influenced by threshold segmentation, resulting in large deviation parameters of parabola identification. Therefore, it is necessary to design a brand-new road boundary intelligent identification method based on image segmentation and edge features. That is to say, the road boundary is extracted by using the image edge features, and an intelligent road boundary identification algorithm is designed in combination with image segmentation, thus completing the intelligent road boundary identification. The experimental results show that the intelligent road boundary recognition method based on image segmentation and edge features has good recognition effect, reliability and certain application value, and has made certain contributions to improving driving safety.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.