docs: update readme and description

Signed-off-by: badai-nguyen <[email protected]>
autowarefoundation · Jan 16, 2025 · d3215c5 · d3215c5
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diff --git a/perception/autoware_tensorrt_yolox/README.md b/perception/autoware_tensorrt_yolox/README.md
@@ -4,6 +4,8 @@
 
 This package detects target objects e.g., cars, trucks, bicycles, and pedestrians and segment target objects such as cars, trucks, buses and pedestrian, building, vegetation, road, sidewalk on a image based on [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) model with multi-header structure.
 
+Additionally, the package also supports traffic light detection by switching onnx file which target classes listed on respective `label_file`. Currently 0: `unknown`, 1: `car_traffic_light` and 2: `pedestrian_traffic_light`.
+
 ## Inner-workings / Algorithms
 
 ### Cite
@@ -22,12 +24,12 @@ Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun, "YOLOX: Exceeding YOLO Se
 
 ### Output
 
-| Name             | Type                                               | Description                                                         |
-| ---------------- | -------------------------------------------------- | ------------------------------------------------------------------- |
-| `out/objects`    | `tier4_perception_msgs/DetectedObjectsWithFeature` | The detected objects with 2D bounding boxes                         |
-| `out/image`      | `sensor_msgs/Image`                                | The image with 2D bounding boxes for visualization                  |
-| `out/mask`       | `sensor_msgs/Image`                                | The semantic segmentation mask                                      |
-| `out/color_mask` | `sensor_msgs/Image`                                | The colorized image of semantic segmentation mask for visualization |
+| Name             | Type                                               | Description                                                                                           |
+| ---------------- | -------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
+| `out/objects`    | `tier4_perception_msgs/DetectedObjectsWithFeature` | The detected objects or traffic light with 2D bounding boxes                                          |
+| `out/image`      | `sensor_msgs/Image`                                | The image with 2D bounding boxes for visualization                                                    |
+| `out/mask`       | `sensor_msgs/Image`                                | The semantic segmentation mask (only effective for semseg model)                                      |
+| `out/color_mask` | `sensor_msgs/Image`                                | The colorized image of semantic segmentation mask for visualization (only effective for semseg model) |
 
 ## Parameters
 
@@ -45,6 +47,14 @@ The label contained in detected 2D bounding boxes (i.e., `out/objects`) will be
 - BICYCLE
 - MOTORCYCLE
 
+or
+
+- UNKNOWN
+- CAR_TRAFFIC_LIGHT
+- PEDESTRIAN_TRAFFIC_LIGHT
+
+for traffic light detector onnx model.
+
 If other labels (case insensitive) are contained in the file specified via the `label_file` parameter,
 those are labeled as `UNKNOWN`, while detected rectangles are drawn in the visualization result (`out/image`).
 

diff --git a/perception/autoware_tensorrt_yolox/config/yolox_traffic_light_detector.param.yaml b/perception/autoware_tensorrt_yolox/config/yolox_traffic_light_detector.param.yaml
@@ -4,7 +4,6 @@
 
     model_path: "$(var data_path)/tensorrt_yolox/$(var model_name).onnx"
     label_path: "$(var data_path)/tensorrt_yolox/car_ped_tl_detector_labels.txt"
-    color_map_path: "$(var data_path)/tensorrt_yolox/semseg_color_map.csv"
     score_threshold: 0.35
     nms_threshold: 0.7
 
@@ -20,7 +19,8 @@
     calibration_image_list_path: "" # Path to a file which contains path to images. Those images will be used for int8 quantization.
 
 
-    # default params, effective only for semseg model
+    # default params of tensorrt_yolox package, only effective for semseg model
+    color_map_path: "$(var data_path)/tensorrt_yolox/semseg_color_map.csv"
     is_roi_overlap_segment: false
     overlap_roi_score_threshold: 0.3
     is_publish_color_mask: false