Object Detection in Refrigerator and Calorie Estimation using EfficientDet1 and YOLOv5S Techniques on Mobile Devices




Agarwal, Rohit

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In this research, we developed a tool to automate object detection and calorie estimation of food items in home refrigerators compatible with both windows and mobile devices. This tool detects objects using the state-of-the-art one-stage methods EfficientDet1 and Yolov5s (You look only once). The tool’s performance is assessed by comparing speed, accuracy, time, and mean average precision (mAP) to learn the advantages in real-time applications. Open-source frameworks such as TensorFlow or PyTorch are used to train the object detection models. Using these models, the object detection of items is identified and classified. A Python algorithm is developed to count the number of items and their calorie estimations. The algorithm automates the process by generating the inventory of items in the refrigerator and sending it to users at their designated e-mail addresses. If any of the desired food items are not present or fall below their threshold values, then the items are flagged. The experimental results indicate that object detection models (EfficientDet1 and Yolov5s) are suitable to run on smaller models for real-time applications. The associated web-based graphical user interface (GUI) (compatible with mobile devices as well) displays the list of items in a detected image, the calorie estimation of each item, and the list of missing items needed for further action. Though our GUI Python code is tested on the local computer, it can be repurposed on any platform such as mobile or embedded devices. The advantage of automatic detection of refrigerator items through the mobile app is helpful in making life easier and healthier, especially for diet-conscious people.



Object Detection