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Safety Equipment Detection

Safety Equipment Detection Node Documentation
The Safety Equipment Detection node in AugeLab Studio is used to detect and count safety equipment in input images. It utilizes the YOLOv4 object detection algorithm to identify safety equipment such as helmets, safety vests, safety goggles, and safety gloves.
Node Overview
The Safety Equipment Detection node takes an input image and performs safety equipment detection. It outputs the processed image with bounding boxes around the detected safety equipment and provides counts for each type of safety equipment detected.
Node Interaction
1.Drag and drop the Safety Equipment Detection node from the node library onto the canvas in AugeLab Studio.
2.Connect the input image data to the node's input socket.
3.Configure the node by adjusting the confidence threshold using the provided slider in the node's user interface.
4.Run the pipeline or execute the node to process the input image and detect safety equipment.
5.View the output image with bounding boxes around the detected safety equipment.
6.Retrieve the counts for each type of safety equipment from the corresponding output sockets for further analysis or processing.
Implementation Details
The Safety Equipment Detection node uses the YOLOv4 object detection algorithm to detect safety equipment in the input image. It follows these steps during its implementation:
1.Initialization:
The node initializes the YOLOv4 object detector using pre-trained weights and configuration files.
The weights, configuration file, and class file for safety equipment detection are loaded.
2.User Interface:
The node provides a slider widget to adjust the confidence threshold for safety equipment detection.
The confidence threshold determines the minimum confidence level required for an object to be detected.
3.Safety Equipment Detection:
The node receives an input image and performs safety equipment detection using the YOLOv4 detector.
It processes the image and detects safety equipment objects based on the pre-trained model.
The detected safety equipment objects are classified into categories such as helmets, safety vests, safety goggles, and safety gloves.
4.Counting:
The node counts the number of each type of safety equipment detected in the input image.
It provides separate output counts for helmets, safety vests, safety goggles, and safety gloves.
Additionally, it counts the number of instances where no helmet, safety vest, safety goggle, or safety glove is detected.
5.Output:
The node outputs the processed image with bounding boxes around the detected safety equipment.
It also provides counts for each type of safety equipment and instances where no safety equipment is detected.
Usage
1.Drag and drop the Safety Equipment Detection node from the node library onto the canvas in AugeLab Studio.
2.Connect the input image data to the node's input socket.
3.Configure the node by adjusting the confidence threshold using the provided slider in the node's user interface.
4.Run the pipeline or execute the node to process the input image and detect safety equipment.
5.View the output image with bounding boxes around the detected safety equipment.
6.Retrieve the counts for each type of safety equipment from the corresponding output sockets for further analysis or processing.
Notes
The Safety Equipment Detection node provides a convenient way to detect and count safety equipment in input images.
It utilizes the YOLOv4 object detection algorithm for accurate and efficient safety equipment detection.
The node supports the detection of safety equipment such as helmets, safety vests, safety goggles, and safety gloves.
You can adjust the confidence threshold to control the sensitivity of the safety equipment detection.
The node outputs the processed image with bounding boxes around the detected safety equipment for visualization.
It also provides counts for each type of safety equipment detected, as well as instances where no safety equipment is detected.
The Safety Equipment Detection node can be used in various applications, including safety monitoring, industrial inspections, and more.
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Last modified 4mo ago