Efficient Food Image Segmentation Using YOLOv5: A Step Towards Automated Food Recognition

Obesity rates have surged globally, necessitating effective weight management strategies. Documenting dietary intake is pivotal for weight loss management. This paper proposes a novel deep learning-based approach, Food Image Segmentation, tailored for automated food image segmentation on custom datasets. Leveraging convolutional neural networks (CNNs) enhanced with transfer learning and attention mechanisms, our model achieves exceptional segmentation accuracy. Extensive experimentation validates its robustness and effectiveness, facilitating precise dietary assessment. Integrated into a user-friendly interface, our solution empowers individuals to capture food images and receive real-time nutritional analysis, aiding informed dietary choices. We address the challenge of deriving food information from images by proposing a CNN-based food image recognition algorithm. Our findings demonstrate significant advancements in automated food image segmentation, offering a promising avenue for personalized dietary management. We have achieved a mean average precision ranging from 0.407 to 0.895 in our work on various food instances.