Image processing application in ensuring food safety

Good hygiene practices are the basis for effective food safety management. For good hygiene practices, food business operators should be aware of all kinds of factors that cause spoilage and contamination due to chemical, biological and physical reasons, and ensure food hygiene by taking the necessary precautions to protect the health of the consumer. Good hygiene practices such as cleaning, sterilization, waste management, equipment maintenance and control, personnel personal hygiene are included in preventing foodborne problems by ensuring food hygiene. In this context, the use of personal protective equipment (PPE) by personnel is considered among the basic preventive measures used to ensure food safety. The use of PPE such as aprons, gloves, hats and masks by personnel in food preparation and service areas should be based on continuity. Personnel monitoring is important in this regard, as most people pay more attention to complying with the rules when they know they are being controlled. However, continuity of such follow-up is difficult and can sometimes bring extra workload. With the development of artificial intelligence, image processing applications provide great benefits in realizing automatic and continuous control. Within the scope of the thesis study, it is aimed to detect non-conformities regarding the use of PPE by using image processing technology in order to ensure food safety and hygiene during the preparation and cooking stages of food in restaurant kitchens. To achieve this, the deep learning-based Yolo algorithm, which has real-time, fast processing power and high accuracy rates, was preferred. In the study, PPE used in the food industry such as masks, uniforms, gloves and hats were detected using Yolov5, Yolov8 and Yolov9 object detection algorithms at different epoch numbers. The best results were obtained using the Yolov9 algorithm, and a high success rate was achieved by reaching 88.5% precision, 90.9% recall and 92.7% mAP value.