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 two different methods to perform object detection:
Using MobileNetV2 SSD FPN​
Using FOMO​
Specifications
MobileNetV2 SSD FPN
FOMO
Labelling method
Bounding boxes
Bounding Boxes
Input size
320x320
Square (any size)
Image format
RGB
Greyscale & RGB
Output
Bounding boxes
Centroids
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

Specifications	MobileNetV2 SSD FPN	FOMO
Labelling method	Bounding boxes	Bounding Boxes
Input size	320x320	Square (any size)
Image format	RGB	Greyscale & RGB
Output	Bounding boxes	Centroids
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

Transfer learning (Keyword Spotting)

MobileNetV2 SSD FPN

Last modified 6mo ago