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AISOMA AG
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Computer Vision: Object Detection and Segmentation with Mask R-CNN

https://youtu.be/akK5ui-vel0

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Computer Vision: Object Detection and Segmentation with Mask R-CNN

https://youtu.be/akK5ui-vel0

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Computer Vision: Object Detection and Segmentation with Mask R-CNN

https://youtu.be/akK5ui-vel0

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Computer Vision: Object Detection and Segmentation with Mask R-CNN

https://youtu.be/akK5ui-vel0

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Case Study:

Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+

without connecting to any external web/cloud services

Our approach:

1. Object detection
HoughCircle Detection (OpenCV)

2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).

Achieved validation accuracy: 93.3%*

Test accuracy achieved: 93.4%*

Runtime for classification: 50ms

*strongly dependent on lighting conditions and camera settings

https://youtu.be/XVvfcj_F_uc

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Case Study:

Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+

without connecting to any external web/cloud services

Our approach:

1. Object detection
HoughCircle Detection (OpenCV)

2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).

Achieved validation accuracy: 93.3%*

Test accuracy achieved: 93.4%*

Runtime for classification: 50ms

*strongly dependent on lighting conditions and camera settings

https://youtu.be/XVvfcj_F_uc

Post has attachment
Case Study:

Real-time object detection and classification with DeepLearning on the Raspberry Pi 3 B+

without connecting to any external web/cloud services

Our approach:

1. Object detection
HoughCircle Detection (OpenCV)

2. Object classification
A specially optimized deep learning network is used, which is also performant on computers with limited resources without GPU and can nevertheless achieve relatively high accuracies. Only a small training and test data set are available for the eight different euro coins: 1281 photos for training, 707 for testing. Since the training data set is minimal, so-called transfer learning is used. A pre-trained deep learning network is used, which was trained on the ImageNet training dataset (approx. 1.2 million images from 1000 categories). To make the classification more robust against rotations, brightness, contrast etc., the training images were additionally rotated randomly, and brightness and contrast were changed (data augmentation).

Achieved validation accuracy: 93.3%*

Test accuracy achieved: 93.4%*

Runtime for classification: 50ms

*strongly dependent on lighting conditions and camera settings

https://youtu.be/XVvfcj_F_uc

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There's a bit of truth in it. Just a bit... ;-)
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