![]() Therefore, kitchen standard dress detection is particularly important.Īt present, there are only a few studies on kitchen wear detection, and these studies are based on target detection. If the chef is suffering from a disease, the risk of virus transmission will be increased greatly. Assuming the chef does not wear a mask, the droplets from the chef’s breath or coughing will fall onto the food. Assuming that the chef does not wear a chef’s hat and apron, the dirty things on the body of the chef will pollute the food. Chefs should wear chef hats, aprons, and masks, so the kitchen standard dress includes the chef’s hat, apron, and mask. Through the standardization of chefs’ wearing standards, the safety levels of food will be improved, and the risk of disease transmission and food poisoning will be reduced. Food safety standards propose that the wearing standards for chefs must be standardized strictly. The latest Food Safety Law also stipulates that the relevant personnel should wear clean work clothes and hats when cooking. ![]() To solve the problem of food safety, the CFDA has launched a “transparent kitchen and stoves” campaign, showing the details of cooking through video displays, open kitchens, and other ways. ![]() Therefore, the proposed detection method provided strong technical support for kitchen hygiene and food safety. 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. Among them, the combination of YOLOv5 and DeepStream SDK effectively improved the accuracy and effectiveness of standard dress detection in the complex kitchen background. 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. 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. 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. ![]()
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