Count Objects on a Conveyor Belt Using Computer Vision

In a detailed guide, Samuel A. explains an end-to-end process for detecting and counting objects on a conveyor belt using computer vision, specifically focusing on small components like bolts and nuts. The approach involves collecting and annotating a dataset, training a custom object detection model, and building a workflow application to accurately count objects in real-time by processing video streams. The guide highlights the use of Roboflow's tools to automate data annotation and streamline the model training process, leveraging features such as automated labeling, model selection, and cloud-based infrastructure. To prevent duplicate counts, the workflow integrates ByteTrack for object tracking, allowing for accurate counting as objects cross defined virtual lines on the conveyor belt. The final step involves deploying the solution locally, enabling real-time video processing and updating counts as objects pass through the line counter. This comprehensive methodology is applicable to various manufacturing environments, enhancing inventory management and quality control.