The document discusses the TensorFlow Object Detection API, which facilitates quick computer vision experiments and provides a model zoo with pre-trained models for various tasks. It highlights the use of MobileNetV2 with SSD for efficient object detection and describes alternative models like R-CNN and YOLO. Additionally, it offers guidance on modifying detection code for specific project needs and references relevant articles and libraries.

1. TensorFlow Object
Detection

2. Defining the problem

3. Library
TF Object Detection offers an API to perform quick
experiments regarding Computer Vision problems
https://github.com/tensorflow/models/tree/master/research/object_detection

4. Model Zoo
For each task, a pre-trained model in a given dataset is offered
Choose one or other considering the accuracy / speed tradeoff
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc
/detection_model_zoo.md

5. SSD Mobilenet
SSD: Single Shot MutiBox Detector
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNetV2: Inverted Residuals and Linear Bottlenecks

6. Object Detection
Different
Loss
Function
may be
applied

7. Convolutional operation

8. Convolutional operation

9. Convolutional operation

10. Convolutional operation

11. MobileNet v2

12. 1x1 Convolution
Operation applied to
every input channel
used to change the
number of channels in
the output

13. ResNets
As a DNN becomes deeper, the risk of having vanishing / exploding gradients
increases. Residual Nets aims to address this problem
[He et al., 2015, Deep Residual Learning for Image Recognition]

14. MobileNet v2

15. MobileNet v2

16. MobileNet v2

17. MobileNet v2 as a Feature Extractor

18. Single Shot Detector (SSD)
● Neural Net built in top of MobileNet v2 (or others)
● Uses the features extracted to perform object detection

19. SSD in top of VGG
● Generation of Priors: 19x19, 10x10, 5x5,
3x3, 2x2 and 1x1

20. Intersection over Union (IoU)
Find which priors (anchors) match which ground truth boxes

21. Non-Maxima Suppression
● Choose the box with highest prediction score
● Compare it with the others with IoU and delete the most overlapping ones
● Repeat

22. Alternative models
● Region-based Convolutional Network (R-CNN)
● Fast Region-based Convolutional Network (Fast R-CNN)
● Faster Region-based Convolutional Network (Faster R-CNN)
● Region-based Fully Convolutional Network (R-FCN)
● YOLOv3
● RetinaNet
Article: Review of Deep Learning Algorithms for Object Detection

23. Project
Define which classes of interest to detect
Use of a video processing library: OpenCV
Modify the detection code to adjust it to your project’s needs
Made an executable version with PyInstaller
https://github.com/xavialex/object-detection

24. Acknowledgments / References
● https://machinethink.net/blog/mobilenet-v2/
● https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html
● https://medium.com/@smallfishbigsea/mobilenet-based-single-short-multi-box
-detector-in-pytorch-onnx-and-caffe2-2509bd038427