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

We use MobileNet-v2 Model.

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 pixels

Result ( Image )