From 21c9fea44c104fed04d0ce2288d2de3f55f8da19 Mon Sep 17 00:00:00 2001
From: Shin-kyoto <58775300+Shin-kyoto@users.noreply.github.com>
Date: Wed, 22 Nov 2023 14:55:15 +0900
Subject: [PATCH] docs: modify description about 2d detector
Signed-off-by: Shin-kyoto <58775300+Shin-kyoto@users.noreply.github.com>
---
.../autoware-architecture/perception/index.md | 26 +++++++++----------
1 file changed, 13 insertions(+), 13 deletions(-)
diff --git a/docs/design/autoware-architecture/perception/index.md b/docs/design/autoware-architecture/perception/index.md
index e3196c194a6..8e9973030de 100644
--- a/docs/design/autoware-architecture/perception/index.md
+++ b/docs/design/autoware-architecture/perception/index.md
@@ -78,19 +78,19 @@ As mentioned in the goal session, this perception module is designed to be exten
## Supported Functions
-| Feature | Description | Requirements |
-| ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- |
-| LiDAR DNN based 3D detector | This module takes point clouds as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. | - Point Clouds |
-| Camera DNN based 2D detector | This module takes camera image as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. | - Camera Images |
-| LiDAR Clustering | This module performs clustering of point clouds and shape estimation to achieve object detection without labels. | - Point Clouds |
-| Semi-rule based detector | This module performs object detection using information from both images and point clouds, and it consists of two components: LiDAR Clustering and Camera DNN based 2D detector. | - Output from Camera DNN based 2D detector and LiDAR Clustering |
-| Object Merger | This module integrates results from various detectors. | - Detected Objects |
-| Interpolator | This module stabilizes the object detection results by maintaining long-term detection results using Tracking results. | - Detected Objects
- Tracked Objects |
-| Tracking | This module gives ID and estimate velocity to the detection results. | - Detected Objects |
-| Prediction | This module predicts the future paths (and their probabilities) of dynamic objects according to the shape of the map and the surrounding environment. | - Tracked Objects
- Vector Map |
-| Obstacle Segmentation | This module identifies point clouds originating from obstacles that the ego vehicle should avoid. | - Point Clouds
- Point Cloud Map |
-| Occupancy Grid Map | This module detects blind spots (areas where no information is available and where dynamic objects may jump out). | - Point Clouds
- Point Cloud Map |
-| Traffic Light Recognition | This module detects the position and state of traffic signals. | - Camera Images
- Vector Map |
+| Feature | Description | Requirements |
+| ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- |
+| LiDAR DNN based 3D detector | This module takes point clouds as input and detects objects such as vehicles, trucks, buses, pedestrians, and bicycles. | - Point Clouds |
+| Camera DNN based 2D detector | This module takes camera images as input and detects objects such as vehicles, trucks, buses, pedestrians, and bicycles in the two-dimensional image space. It detects objects within image coordinates and providing 3D coordinate information is not mandatory. | - Camera Images |
+| LiDAR Clustering | This module performs clustering of point clouds and shape estimation to achieve object detection without labels. | - Point Clouds |
+| Semi-rule based detector | This module detects objects using information from both images and point clouds, and it consists of two components: LiDAR Clustering and Camera DNN based 2D detector. | - Output from Camera DNN based 2D detector and LiDAR Clustering |
+| Object Merger | This module integrates results from various detectors. | - Detected Objects |
+| Interpolator | This module stabilizes the object detection results by maintaining long-term detection results using Tracking results. | - Detected Objects
- Tracked Objects |
+| Tracking | This module gives ID and estimate velocity to the detection results. | - Detected Objects |
+| Prediction | This module predicts the future paths (and their probabilities) of dynamic objects according to the shape of the map and the surrounding environment. | - Tracked Objects
- Vector Map |
+| Obstacle Segmentation | This module identifies point clouds originating from obstacles that the ego vehicle should avoid. | - Point Clouds
- Point Cloud Map |
+| Occupancy Grid Map | This module detects blind spots (areas where no information is available and where dynamic objects may jump out). | - Point Clouds
- Point Cloud Map |
+| Traffic Light Recognition | This module detects the position and state of traffic signals. | - Camera Images
- Vector Map |
## Reference Implementation