A look at open source image recognition technology using COCO and YOLO

A look at open source image recognition technology using COCO and YOLO

A PhD student from Louisiana State University, Shayan Shams, had set up a large monitor displaying a webcam image. Overlaid on the image were colored boxes with labels. The labels identified objects on a table. As each object was moved on the table, its label followed. Objects that were off-camera into the field of view, and the system identifies them too.

The entire thing came together from open software and data. Shams used the Common Objects in Context (COCO) dataset for object recognition, reducing unnecessary classes to enable it to run on less powerful hardware. "Detecting some classes, such as airplane, car, bus, truck, and so on in the SC exhibition hall was not necessary," he explained. To do the actual detection, Shams used the You Only Look Once (YOLO) real-time object detection system.

Working under LSU professor Seung-Jong Park, Shams is applying his research to the field of biomedical imaging. In one project, he applied deep learning to mammography: By analyzing mammogram images, medical professionals can reduce the number of unnecessary biopsies they perform. This not only lowers medical costs, but it saves patients stress.

If you're interested in experimenting with image-recognition technology, a Raspberry Pi with a webcam is sufficient hardware (although the recognition may not be immediate).

See https://opensource.com/article/18/5/state-of-image-recognition

#imagerecognition #COCO #YOLO