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Object Detection

Object Detection Node Documentation
The Object Detection node in AugeLab Studio is used to detect objects of specific classes in an input image. It applies an object detection model to identify and localize objects, providing information about their count, locations, and sizes.
Node Overview
The Object Detection node takes an image as input and applies an object detection model to identify and localize objects of specific classes. It provides the following outputs:
Processed Image: The input image with the object detection results visualized.
Object Count: The total number of objects detected.
Object Locations: The coordinates of the bounding boxes around the detected objects.
Object Sizes (w, h): The width and height of the bounding boxes around the detected objects.
Inputs
The Object Detection node requires the following input:
Image: The input image to be processed.
Outputs
The Object Detection node provides the following outputs:
Processed Image: The input image with the object detection results visualized.
Object Count: The total number of objects detected.
Object Locations: The coordinates of the bounding boxes around the detected objects.
Object Sizes (w, h): The width and height of the bounding boxes around the detected objects.
Node Interaction
1.Drag and drop the Object Detection node from the node library onto the canvas in AugeLab Studio.
2.Connect the input image to the Image input socket of the Object Detection node.
3.Configure the node properties:
Confidence Threshold (%): Set the confidence threshold for object detection. This determines the minimum confidence level required for an object to be considered detected.
Select Detection Class: Choose the desired object class or category to detect.
4.The output sockets provide the following information:
Processed Image: The input image with the object detection results visualized.
Object Count: Get the total number of objects detected.
Object Locations: Get the coordinates of the bounding boxes around the detected objects.
Object Sizes (w, h): Get the width and height of the bounding boxes around the detected objects.
5.Connect the output sockets to other nodes for further processing or analysis.
Implementation Details
The Object Detection node is implemented as a subclass of the CalcNode base class. It overrides the evalImplementation method to perform the object detection.
The node reads the input image from the input socket.
The object detection model is initialized.
The confidence threshold and selected detection class are obtained from the node's configuration.
The object detection model is applied to the input image, and the results are obtained.
The processed image with the object detection results is returned, along with the count, locations, and sizes of the detected objects.
Usage
1.Drag and drop the Object Detection node from the node library onto the canvas in AugeLab Studio.
2.Connect the input image to the Image input socket of the Object Detection node.
3.Configure the node properties:
Confidence Threshold (%): Set the confidence threshold for object detection. Adjust this value to control the sensitivity of the object detection.
Select Detection Class: Choose the desired object class or category to detect. You can select "All" to detect objects of all classes.
4.The output sockets provide the following information:
Processed Image: View the input image with the object detection results visualized.
Object Count: Get the total number of objects detected.
Object Locations: Get the coordinates of the bounding boxes around the detected objects.
Object Sizes (w, h): Get the width and height of the bounding boxes around the detected objects.
5.Connect the output sockets to other nodes for further processing or analysis.
6.Continue building your pipeline by connecting other nodes as needed.
Notes
The Object Detection node applies an object detection model to identify and localize objects in an image.
The confidence threshold can be adjusted to control the sensitivity of the object detection. Higher values result in stricter detection.
The Object Detection node provides both the processed image with the object detection results and additional information about the detected objects.
Use the outputs of the Object Detection node to perform further analysis or visualization, or as input for other nodes in the pipeline.
The Object Detection node supports the detection of specific classes or categories of objects. You can select a desired detection class or choose "All" to detect objects of all classes.
The Object Detection node leverages the YOLOv4 model for object detection.
Consider combining the Object Detection node with other nodes to create a more comprehensive AI application or analysis pipeline.
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Last modified 4mo ago