Computer Vision at The Edge: Where it's Headed in 2019

The answer is at the edge. The accuracy of object detection and facial recognition continues to improve, and the number of readily available options based on state-of-the-art deep learning technologies including convolutional and recurrent neural networks continues to increase. The improvements come at a cost - an increase in the complexity and processing requirements of the technologies. YOLOV3 for example, a popular object recognition model, has a 106 layer fully convolutional underlying architecture, more than doubling from the previous version. Other models, such as RetinaNet and SSD variants are also showing huge strides in accuracy, but again, at the cost of increased complexity and reduced performance.