[docs] Update "About Pipelines" list

docs/source/docs/pipelines/about-pipelines.md

@@ -10,20 +10,32 @@ A vision pipeline represents a series of steps that are used to acquire an image
 
 ## Types of Pipelines
 
-### Reflective
+### AprilTag / AruCo
+
+This pipeline type is based on detecting AprilTag fiducial markers. More information about AprilTags can be found in the [WPILib documentation](https://docs.wpilib.org/en/stable/docs/software/vision-processing/apriltag/apriltag-intro.html). This pipeline provides easy to use 3D pose information which allows localization.
+
+:::{note}
+In order to get 3D Pose data about AprilTags, you are required to {ref}`calibrate your camera<docs/calibration/calibration:Calibrating Your Camera>`.
+:::
+
+### Object Detection
+
+This pipeline type is based on detecting objects using a neural network. The object detection pipeline uses a pre-trained model to detect objects in the camera stream. In the object detection tab of this pipeline type, you can configure the confidence threshold and apply filters based on area and width/height ratio.
 
-This is the most common pipeline type and it is based on detecting targets with retroreflective tape. In the contours tab of this pipeline type, you can filter the area, width/height ratio, fullness, degree of speckle rejection.
+:::{note}
+This pipeline type is only available on the Orange Pi 5/5+ coprocessors due to its Neural Processing Unit used by PhotonVision to support running ML-based object detection.
+:::
 
 ### Colored Shape
 
 This pipeline type is based on detecting different shapes like circles, triangles, quadrilaterals, or a polygon. An example usage would be detecting yellow PowerCells from the 2020 FRC game. You can read more about the specific settings available in the contours page.
 
-### AprilTag / AruCo
+### Reflective
 
-This pipeline type is based on detecting AprilTag fiducial markers. More information about AprilTags can be found in the WPILib documentation. While being more performance intensive than the reflective and colored shape pipeline, it has the benefit of providing easy to use 3D pose information which allows localization.
+This pipeline type is based on detecting targets with 3M retro-reflective tape. In the contours tab of this pipeline type, you can filter the area, width/height ratio, fullness, degree of speckle rejection.
 
 :::{note}
-In order to get 3D Pose data about AprilTags, you are required to {ref}`calibrate your camera<docs/calibration/calibration:Calibrating Your Camera>`.
+This pipeline type is not used anymore due to FRC's removal of retro-reflective tape from the game. It is still available as a pipeline for legacy purposes.
 :::
 
 ## Note About Multiple Cameras and Pipelines
@@ -44,3 +56,9 @@ AprilTag / AruCo Pipelines have 3 steps:
 1. Input: This is the same as the above.
 2. AprilTag: This step include AprilTag specific tuning parameters, such as decimate, blur, threads, pose iterations, and more.
 3. Output: This is the same as the above.
+
+Object Detection Pipelines have 3 steps:
+
+1. Input: This is the same as the above.
+2. Object Detection: This step allows you to filter results from the neural network, such as confidence, area, and width/height ratio. The end goal of this step is to filter out any false positives.
+3. Output: This is the same as the above.