Allergen30: Detecting Food Items with Possible Allergens Using Deep Learning-Based Computer Vision

Food allergies impose a significant health concern on the community. A small number of certain food items can cause an allergic reaction within the human body. The symptoms can range from mild hives or itchiness to life-threatening anaphylaxis. In most cases, such reactions can be prevented by simply being aware of the allergen-based food items and avoiding the consumption of the same. We are among the first research attempts to train a deep learning–based object detection model to detect the presence of such food items within an image. We introduce our Allergen30 dataset, which hosts more than 6,000 annotated images of 30 commonly used food items that can trigger an adverse reaction. We report the comparison of multiple variants of the current state-of-art object detection methods, YOLOv5 and YOLOR. Furthermore, we qualitatively analyzed the performance of these methods by surveying the predictions made on the test dataset images.