A Kitchen Standard Dress Detection Method Based on the YOLOv5s Embedded Model
Source abstract
Abstract
In order to quickly and accurately detect whether a chef is wearing a hat and mask, a kitchen standard dress detection method based on the YOLOv5s embedded model is proposed. Firstly, a complete kitchen scene dataset was constructed, and the introduction of images for the wearing of masks and hats allows for the low reliability problem caused by a single detection object to be effectively avoided. Secondly, the embedded detection system based on Jetson Xavier NX was introduced into kitchen standard dress detection for the first time, which accurately realizes real-time detection and early warning of non-standard dress. Among them, the combination of YOLOv5 and DeepStream SDK effectively improved the accuracy and effectiveness of standard dress detection in the complex kitchen background. Multiple sets of experiments show that the detection system based on YOLOv5s has the highest average accuracy of 0.857 and the fastest speed of 31.42 FPS. Therefore, the proposed detection method provided strong technical support for kitchen hygiene and food safety.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Image Enhancement Techniques · Textile materials and evaluations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Information Center of Yunnan Administration for Market Regulation, Kunming 650228, China
School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
School of Big Data, Baoshan University, Baoshan 678000, China
School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, China
Funding
Yunnan Provincial Market Supervision Bureau IOT perception platform construction project · PhD research startup foundation of Yunnan Normal University
References (28)
Show source-provided references
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