Image Object Detection ‐ Detecto
Workshops sessions are recorded and posted to the UA Datalab Youtube Channel about 1 week after the live session.
Detecto is a light weight python library built on top of deep learning framework Pytorch.
By default, Detecto uses the convolutional neural network architecture Faster R-CNN ResNet-50 FPN. The architecture was pre-trained on the COCO (common objects in context) dataset which contains over 330,000 images, each annotated with 80 object classes (e.g., person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, kite, knife, spoon, tv, book). The images and labels are generally not from aerial view points. Therefore, Detecto is not ready to identify objects in aerial images out-of-the-box. It has to be trained to do so.
A convolutional neural network can be trained to identify objects in an image analogously to how humans can identify objects. For example, look at the aerial images of tree species above. The human brain can easily tell that these are probably 3 distinct tree species (from left to right: ponderosa pine, mesquite, willow). We can look at features such as crown shape and leaf architecture to help us identify a tree. For a computer, however, this is very challenging.
Traditional image analysis approaches such as looking at color values of individual pixels or grouping adjacent pixels based on similar values (i.e., objects), will both likely to fail at the task of identifying trees in high-resolution images.
Please watch these VERY useful videos to understand how Convolutional Neural Networks operate:
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What are Convolutional Neural Networks (CNNs)? from IBM Technology
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But what is a neural network? from 3Blue1Brown
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How Convolutional Neural Networks Work from Brandon Rohrer
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Convolutional Neural Networks Explained (CNN Visualized) from Futurology
CNN models can take a few different approaches to image classification. Deepforest specifically carries out object detection.
Object labels need to be in an xml file using the PASCAL VOC format. Try [Label Studio](https://labelstudio.io) tool to create labels.
NWPU VHR-10 dataset ( Cheng et al., 2014a ). 1 This is a challenging 10-class geospatial object detection dataset, which can be used for both single class and multi-class objects detection. These ten classes of objects are airplane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, and vehicle. This dataset contains totally 800 VHR optical RSIs, from which 757 airplanes, 302 ships, 655 storage tanks, 390 baseball diamonds, 524 tennis courts, 159 basketball courts, 163 ground track fields, 224 harbors, 124 bridges, and 477 vehicles were manually annotated with axis-aligned bounding boxes used for ground truth. 715 color images were acquired from Google Earth with the spatial resolution ranging from 0.5-m to 2-m, and 85 pansharpened color infrared (CIR) images were acquired from Vaihingen data with a spatial resolution of 0.08-m.
https://huggingface.co/datasets/satellite-image-deep-learning/SODA-A
One option to run the notebook is in Google Colab. This is probably the easiest option to get started. Google provides you with a virtual machine with optional (but limited) GPU resources.
