Food Manufacturing Defect Detection with Lucky Charms

Jake Barone Malis explores the process of using computer vision for food manufacturing defect detection, inspired by the idea of using a robotic arm to separate marshmallows from Lucky Charms cereal. The article details two methods for training a dataset: Roboflow Train, an easy-to-use AutoML solution, and the TensorFlow Object Detection API, which offers more control over model training. The dataset, featuring labeled images of marshmallow shapes, is prepared using Roboflow's platform, and the training process is conducted using Google Cloud's TPUs for efficiency. The output is a model capable of accurately identifying marshmallows, which can be deployed for inference via various methods. The article concludes with a preview of future applications, such as integrating the model with a robotic arm to autonomously sort marshmallows from cereal pieces.