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pr-385/design/autoware-architecture/perception/index.html

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 <td>LiDAR DNN based 3D detector</td>
-<td>This module takes point clouds as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles.</td>
+<td>This module takes point clouds as input and detects objects such as vehicles, trucks, buses, pedestrians, and bicycles.</td>
 <td>- Point Clouds</td>
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 <td>Camera DNN based 2D detector</td>
-<td>This module takes camera image as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles.</td>
+<td>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.</td>
 <td>- Camera Images</td>
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@@ -4538,7 +4538,7 @@ <h2 id="supported-functions">Supported Functions<a class="headerlink" href="#sup
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 <td>Semi-rule based detector</td>
-<td>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.</td>
+<td>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.</td>
 <td>- Output from Camera DNN based 2D detector and LiDAR Clustering</td>
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