Object detection (Images)

The two most common image-processing problems are image classification and object detection.

Image classification takes an image as an input and outputs what type of object is in the image. This technique works great, even on microcontrollers, as long as we only need to detect a single object in the image.

On the other hand, object detection takes an image and outputs information about the class and number of objects, position, (and, eventually, size) in the image.

Edge Impulse provides four different methods to perform object detection:

SpecificationsMobileNetV2 SSD FPNFOMONVIDIA TAOYOLOv5

Labelling method

Bounding boxes

Bounding Boxes

Bounding boxes

Bounding boxes

Input size

320x320

Square (any size)

Flexible

Flexible

Image format

RGB

Greyscale & RGB

RGB

RGB

Output

Bounding boxes

Centroids

Bounding boxes

Bounding boxes

MCU

CPU/GPU

Limitations

- Works best with big objects - Models use high compute resources (in the edge computing world) - Image size is fixed

- Works best when objects have similar sizes & shapes - The size of the objects are not available - Objects should not be too close to each other

- Works best on high end MCU

- More compute intensive - Not suitable for all edge devices

Last updated

Revision created

tab only