Temperature monitoring based on image processing for intelligent microwave heating

Li-yan, Cui; Gao, Min; Xiong, Qingyu; Wen, Junhao; Xie, Nan

doi:10.1109/ccdc.2015.7162137

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…The FLIR camera is used in [9] to capture the thermal surface image of the food. In [10] a traditional color Charge-Coupled Device (CCD) camera is proposed to monitor the temperature inside a microwave oven. This may be a low-cost approach but may not be effective.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [8] uses thermaCAM FLIR P65 (FLIR systems, MA, USA) and [9] uses a FLIR camera (Unilever Research Colworth Laboratory, Sharnbrook, UK) for contactless temperature sensing and thermal imaging in real-time. Temperature sensing based on CCD camera is used in [10]. The research in [14] uses the measurement of temperature using the RTD sensor (June Life Inc, CA, USA) that requires physical contact with the food.…”

Section: Related Workmentioning

confidence: 99%

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An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

Khan

2020

ASI

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Whenever food in a microwave oven is heated, the user estimates the time to heat. This estimation can be incorrect, leading the food to be too hot or still cold. In this research, an intelligent microwave oven is designed. After the food is put into the microwave oven and the door is closed, it captures the image of the food, classifies the image and then suggests the food's target temperature by learning from previous experiences, so the user does not have to recall the target food temperature each time the same food is warmed. The temperature of the food is measured using a thermal camera. The proposed microwave incorporates a display to show a real-time colored thermal image of the food. The microwave automatically stops the heating when the temperature of the food hits the target temperature using closed-loop control. The deep learning-based image classifier gradually learns the type of foods that are consumed in that household and becomes smarter in temperature recommendation. The system can classify and recommend target temperature with 93% accuracy. A prototype is developed using a microcontroller-based system and successfully tested.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

Khan

2020

ASI

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“…However, (FLIR) camera is expensive [11] and it can significantly increase the price of the microwave. CCD camera-based temperature sensing is used in [18] to reduce the price. The work in [22] uses temperature measurement using RTD sensor requiring physical contact with the food.…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…However, FLIR cameras are expensive [17] and can significantly increase the price of the microwave oven. In [18], temperature monitoring inside a microwave oven is proposed by using conventional color charge-coupled device (CCD) camera. This method could be low cost, but may not be accurate.…”

Section: Introductionmentioning

confidence: 99%

Smart Microwave Oven with Image Classification and Temperature Recommendation Algorithm

Khan¹

2018

IJECE

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When food is warmed in a microwave oven, the user guesses the estimated time for the heating. This cognitive process of guessing can be incorrect - resulting the final food temperature to be too hot or still cold. In this research, a novel closed-loop microwave oven is designed which automatically suggests the target temperature of a food by learning from previous experiences and the heating stops automatically when the food temperature reaches the target temperature. The proposed microwave captures and classifies the food image, and recommends the target temperature, thus the user does not need to remember the target food temperature each time the same food is warmed. The algorithm gradually learns the type of foods that are used in that household and becomes smarter in the recommendation. The proposed algorithm can recommend target temperature with an accuracy of 86.31% for solid food and 100% for liquid food. A prototype of the proposed microwave is developed using the embedded system and tested.

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Towards an Autonomous Temperature Feedback Microwave Oven with Thermal Imaging

Khan

2018

2018 IEEE International Conference on Electro/Information Technology (EIT)

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Temperature monitoring based on image processing for intelligent microwave heating

Cited by 7 publications

References 5 publications

An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

Smart Microwave Oven with Image Classification and Temperature Recommendation Algorithm

Towards an Autonomous Temperature Feedback Microwave Oven with Thermal Imaging

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