yolov8系列--Hardware-accelerated, deep learned model support.zip

# Isaac ROS Object Detection Hardware-accelerated, deep learned model support for object detection including DetectNet. <div align="center"><img alt="original image" src="https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_object_detection/isaac_ros_object_detection_example.png/" width="300px"/> <img alt="bounding box predictions using DetectNet" src="https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_object_detection/isaac_ros_object_detection_example_bbox.png/" width="300px"/></div> ## Overview [Isaac ROS Object Detection](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_object_detection) contains an ROS 2 package to perform object detection. `isaac_ros_detectnet` provides a method for spatial classification using bounding boxes with an input image. Classification is performed by a GPU-accelerated [DetectNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_detectnet_v2) model. The output prediction can be used by perception functions to understand the presence and spatial location of an object in an image. <div align="center"><a class="reference internal image-reference" href="https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_object_detection/isaac_ros_object_detection_nodegraph.png/"><img alt="image" src="https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_object_detection/isaac_ros_object_detection_nodegraph.png/" width="800px"/></a></div> `isaac_ros_detectnet` is used in a graph of nodes to provide a bounding box detection array with object classes from an input image. A [DetectNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_detectnet_v2) model is required to produce the detection array. Input images may need to be cropped and resized to maintain the aspect ratio and match the input resolution of DetectNet; image resolution may be reduced to improve DNN inference performance, which typically scales directly with the number of pixels in the image. `isaac_ros_dnn_image_encoder` provides a DNN encoder to process the input image into Tensors for the DetectNet model. Prediction results are clustered in the DNN decoder to group multiple detections on the same object. Output is provided as a detection array with object classes. DNNs have a minimum number of pixels that need to be visible on the object to provide a classification prediction. If a person cannot see the object in the image, it’s unlikely the DNN will. Reducing input resolution to reduce compute may reduce what is detected in the image. For example, a 1920x1080 image containing a distant person occupying 1k pixels (64x16) would have 0.25K pixels (32x8) when downscaled by 1/2 in both X and Y. The DNN may detect the person with the original input image, which provides 1K pixels for the person, and fail to detect the same person in the downscaled resolution, which only provides 0.25K pixels for the person. > [!Note] > DetectNet is similar to other popular object detection > models such as YOLOV3, FasterRCNN, and SSD, while being efficient at > detecting multiple object classes in large images. <div align="center"><a class="reference internal image-reference" href="https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_object_detection/isaac_ros_object_detection_example_bboxseg.png/"><img alt="image" src="https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_object_detection/isaac_ros_object_detection_example_bboxseg.png/" width="800px"/></a></div> Object detection classifies a rectangle of pixels as containing an object, whereas image segmentation provides more information and uses more compute to produce a classification per pixel. Object detection is used to know if, and where in a 2D image, the object exists. If a 3D spacial understanding or size of an object in pixels is required, use image segmentation. ## Isaac ROS NITROS Acceleration This package is powered by [NVIDIA Isaac Transport for ROS (NITROS)](https://developer.nvidia.com/blog/improve-perception-performance-for-ros-2-applications-with-nvidia-isaac-transport-for-ros/), which leverages type adaptation and negotiation to optimize message formats and dramatically accelerate communication between participating nodes. ## Performance | Sample Graph<br/><br/> | Input Size<br/><br/> | AGX Orin<br/><br/> | Orin NX<br/><br/> | Orin Nano 8GB<br/><br/> | x86_64 w/ RTX 4060 Ti<br/><br/> | |----------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------| | [DetectNet Object Detection Graph](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/scripts/isaac_ros_detectnet_graph.py)<br/><br/><br/><br/> | 544p<br/><br/><br/><br/> | [232 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_detectnet_graph-agx_orin.json)<br/><br/><br/>11 ms<br/><br/> | [105 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_detectnet_graph-orin_nx.json)<br/><br/><br/>15 ms<br/><br/> | [74.2 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_detectnet_graph-orin_nano.json)<br/><br/><br/>22 ms<br/><br/> | [644 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_detectnet_graph-nuc_4060ti.json)<br/><br/><br/>5.6 ms<br/><br/> | --- ## Documentation Please visit the [Isaac ROS Documentation](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_object_detection/index.html) to learn how to use this repository. --- ## Packages * [`isaac_ros_detectnet`](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_object_detection/isaac_ros_detectnet/index.html) * [Quickstart](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_object_detection/isaac_ros_detectnet/index.html#quickstart) * [Try More Examples](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_object_detection/isaac_ros_detectnet/index.html#try-more-examples) * [ROS 2 Graph Configuration](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_object_detection/isaac_ros_detectnet/index.html#ros-2-graph-configuration) * [