Abstract: An object detection system may be configured for an embedded device, such as a microcontroller. The object detection system may be configured to divide an image into multiple cells arranged in a grid. A cell of the multiple cells may map to a region of one or more pixels in the image. The object detection system may detect in each cell either a background or one of multiple objects that are detectable classes distinct from one another. The background may be detected when none of the multiple objects are detected. The one of the multiple objects may be detected when a centroid of the one of the multiple objects is detected. In some implementations, expected data may be detected in a cell when detecting the data within a range, and anomalous data may be detected in a cell when detecting the data outside of the range.

Abstract: An anomaly detection system may be configured for an embedded device, such as a microcontroller. The anomaly detection system may be configured to receive a dataset from a sensor, such as a camera, a microphone, or an inertial management unit. The anomaly detection system may extract a plurality of features from the dataset. The plurality of features may be configured to train a neural network model, such as a convolutional classifier, to generate one or more classifications. The anomaly detection system may generate an anomaly score based on the plurality of features. The anomaly detection system may trigger an output based on the anomaly score exceeding a range.