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Object detection (Images)
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:
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 |
Last modified 3mo ago