FOMO is a TinyML neural network for real-time object detection

This article is part of our coverage of the latest AI research.

A new machine learning technique developed by researchers at Edge Impulse, an ML modeling platform for the edge, enables real-time object detection to be performed on devices with very small computational capacity and of memory. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications.

Most object detection deep learning models have memory and computational requirements that exceed the capability of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, making it an excellent technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-limited devices that have limited or no internet connectivity.

Image Classification vs Object Detection

TinyML has made great strides in image classification, where the machine learning model only needs to predict the presence of a certain type of object in an image. On the other hand, object detection requires the model to identify more than one object as well as the bounding box of each instance.

Object detection models are much more complex than image classification networks and require more memory.

“We added computer vision support to Edge Impulse in 2020, and we’ve seen a tremendous increase in apps (40% of our projects are computer vision apps),” said Jan Jongboom, CTO at Edge Impulse, at TechTalks. “But with current state-of-the-art models, you could only do image classification on microcontrollers.”

Image classification is very useful for many applications. For example, a security camera might use TinyML image classification to determine whether or not there is a person in the frame. However, much more can be done.

“It was a great nuisance that you were limited to these very basic classification tasks. There is a lot of value in seeing “there are three people here” or “this label is in the top left corner”, for example, counting things is one of the biggest demands we see in the market today. today,” says Jongboom.

Earlier object detection ML models had to process the input image multiple times to locate objects, which made them slow and computationally expensive. Newer models such as YOLO (You Only Look Once) use single-shot detection to provide near real-time object detection. But their memory requirements are still high. Even models designed for high-end applications are difficult to run on small devices.

“YOLOv5 or MobileNet SSD are just incredibly large arrays that will never scale to MCU and barely fit Raspberry Pi class devices,” says Jongboom.

Moreover, these models are bad at detecting small objects and they need a lot of data. For example, YOLOv5 recommends over 10,000 training instances per object class.

The idea behind FOMO is that not all object detection applications require the high precision output provided by state-of-the-art deep learning models. By finding the right trade-off between accuracy, speed, and memory, you can reduce your deep learning models to very small sizes while still keeping them useful.

Instead of detecting bounding boxes, FOMO predicts the center of the object. This is because many object detection applications are only interested in the location of objects in the frame and not their size. Centroid detection is much more computationally efficient than bounding box prediction and requires less data.

sheep-object-detection-bounding-box-vs-centroid

Redefining Object Detection Deep Learning Architectures

FOMO also applies a major structural change to traditional deep learning architectures.

Single-shot object detectors are composed of a set of convolutional layers that extract features and several fully connected layers that predict the bounding box. Convolution layers extract visual features hierarchically. The first layer detects simple things like lines and edges in different directions. Each convolutional layer is usually coupled with a clustering layer, which reduces the size of the layer’s output and retains the most important features in each area.

The output from the clustering layer is then passed to the next convolutional layer, which extracts higher-level features, such as corners, arcs, and circles. As convolution and clustering layers are added, feature maps zoom out and can detect complex elements such as faces and objects.

visualization of neural network layers