Advanced Object Detection
The next-generation of computer vision applications will leverage more than just high-level image classification, these applications will want to know what is happening inside the image. Our algorithms can do more if they know not just that “cars” were in a photo, but can count how many cars passed through an intersection per day. This specific example might be used, for example, in applications where we want to forecast sales at a shopping center based on how many cars drove into a parking lot.
Deep learning has revolutionized the computer vision sector with advances in convolutional neural networks and specific architectures such as "You only look once" (YOLO). YOLO is compelling because it trades only minimal accuracy to be extremely fast at inference time when making object detection predictions. On a Pascal Titan X a YOLO network processes images at 30 FPS and has a mAP of 57.9% on COCO test-dev.
The Patterson Consulting team can integrate next-generation object detection methods into your applications that allow you to not only classify objects in photos but also get accurate bounding box coordinates in the photo as well. This allows a non-phd application engineer to enjoy the benefits of next generation computer vision methods while focusing on building the line of business application.