From 60c758d6df0546b8786c87f878c25ea15c859e06 Mon Sep 17 00:00:00 2001 From: Lukasnol Date: Sun, 3 Nov 2024 11:08:43 +0100 Subject: [PATCH] Update VisionNode_CodeSummary.md --- doc/research/paf24/perception/VisionNode_CodeSummary.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/doc/research/paf24/perception/VisionNode_CodeSummary.md b/doc/research/paf24/perception/VisionNode_CodeSummary.md index a49331d5..161fee95 100644 --- a/doc/research/paf24/perception/VisionNode_CodeSummary.md +++ b/doc/research/paf24/perception/VisionNode_CodeSummary.md @@ -23,7 +23,6 @@ The `VisionNode` class is designed to perform object detection and segmentation - [Minimum X and Y Calculations](#minimum-x-and-y-calculations) - [Models](#models) - ## Overview The `VisionNode` class leverages both PyTorch and Ultralytics pretrained models for object detection and segmentation, integrating features such as bounding boxes, segmentation masks, and distance calculations based on LIDAR data. @@ -31,6 +30,7 @@ The `VisionNode` class leverages both PyTorch and Ultralytics pretrained models ## Class Initialization Upon instantiation, `VisionNode` initializes various configurations, such as: + - **Model Dictionary** (`self.model_dict`): Holds model configurations with different detection and segmentation models from PyTorch and Ultralytics. - **Camera Configurations**: Subscribes to topics for front, rear, left, and right camera views if specified. - **Device Selection**: Automatically selects `CUDA` if available. @@ -92,10 +92,9 @@ These functions subscribe to the camera topics, allowing the node to receive ima - **`min_x`**: Calculates the minimum x-distance for objects in LIDAR data, representing the closest object in the forward direction. - **`min_abs_y`**: Calculates the minimum y-distance in absolute terms, representing the closest object sideways. - ## Models -Following there will be a short overview of the used machine learning and computer vision models. +Following there will be a short overview of the used machine learning and computer vision models. | Model | Techniques | Features | Description | | -------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | @@ -115,4 +114,3 @@ Following there will be a short overview of the used machine learning and comput | yolov8x-seg | YOLO (Segmentation) | Only detection without segmentation. | Extension of YOLO for pixel-precise object detection. | | sam_l | SAM | High accuracy in segmentation for universal applications. | Universal segmentation using SAM technique. | | FastSAM-x | Fast SAM | Faster variant for real-time application requirements. | Fast segmentation using an accelerated SAM model. | -