Raspberry pi 4B Object Detection with TensorFlow and Tensorflow Lite Comparison
What is object detection?
Given an image or a video stream, an object detection model can identify which of a known set of objects might be present and provide information about their positions within the image.
An object detection model is trained to detect the presence and location of multiple classes of objects. For example, a model might be trained with images that contain various pieces of fruit, along with a label that specifies the class of fruit they represent (e.g. an apple, a banana, or a strawberry), and data specifying where each object appears in the image.
When we subsequently provide an image to the model, it will output a list of the objects it detects, the location of a bounding box that contains each object, and a score that indicates the confidence that detection was correct.
Object Detection Models
Run Object Detection Model on Raspberry pi 4B
TensorFlow
Software • Raspberry pi Legacy OS ( Raspbian 10 , 32 bit version ) • Tensorflow 1.14.0 • OpenCV 4.0 • Python 3.7.3 Machine Learning Model : SSD Mobilenet V2 ( FP32 )
Image JPG 1024x636 pixels
Video MP4 960x540 pixelsResult ( Image )
Loading time 8327.86 ms @ test1
Inference time 10245.02 ms
Loading time: 7910.86 ms @ test2
Inference time: 9650.78 ms
Loading time: 7917.60 ms @ test3
Inference time: 9642.02 ms
Result ( Video ) ~ 2 fps
TensorFlow-lite
Loading time: 4.13 ms @ test1
Inference time: 229.85 ms
Loading time: 4.38 ms @ test2
Inference time: 231.55 ms
Result ( Video ) ~ 4-5 fps
TensorFlow and TensorFlow-lite Comparison
Reference
TensorFlow
https://github.com/tensorflow/models
TensorFlow-lite
https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi
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