Vision CNN: Object Detection¶
This ti_vision_cnn
node is versatile deep-learning (DL) inference ROS node that is optimized on DL cores and hardware accelerator of TDA4. The ti_vision_cnn
node supports compute-intensive DL inference operations including 2D object detection and semantic segmentation. Figure 1 shows the high-level block diagram of the applications around the ti_vision_cnn
node, which consists of multiple processing blocks that are deployed on hardware accelerators and DSP processors for pre-processing and post-processing in an optimized manner.
For details of block diagram and parameters of ti_vision_cnn
, please refer to README.md.
Object Detection Demo¶
How to Run the Application in ROS 1¶
[TDA4] To launch object detection demo with playing back a ROSBAG file, run the following inside the Docker container on TDA4 target:
roslaunch ti_vision_cnn bag_objdet_cnn.launch
To process the image stream from a ZED stereo camera:
roslaunch ti_vision_cnn zed_objdet_cnn.launch zed_sn:=SNxxxxx
To process the image stream from a USB mono camera:
roslaunch ti_vision_cnn mono_objdet_cnn.launch
# Alternatively
roslaunch ti_vision_cnn gscam_objdet_cnn.launch
[Visualization on Ubuntu PC] For setting up environment of the remote PC, please follow Docker Setup for ROS 1
To launch visualization:
roslaunch ti_viz_nodes rviz_objdet_cnn.launch
How to Run the Application in ROS 2¶
[TDA4] To launch object detection demo with a ZED stereo camera, run the following inside the Docker container on TDA4 target:
ros2 launch ti_vision_cnn zed_objdet_cnn_launch.py zed_sn:=SNxxxxx
To process the image stream from a USB mono camera:
roslaunch ti_vision_cnn mono_objdet_cnn.launch
# Alternatively
roslaunch ti_vision_cnn gscam_objdet_cnn.launch
[Visualization on Ubuntu PC] For setting up environment of the remote PC, please follow Docker Setup for ROS 2
ros2 launch ti_viz_nodes rviz_objdet_cnn_launch.py