GRAP-UdL-AT/ak_sw_benchmarker

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MIT License
Created over 1 year ago
Updated about 1 year ago

GRAP-UdL-AT/ak_sw_benchmarker

GRAP-UdL-AT

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AKFruitYield: AK_SW_BENCHMARKER Azure Kinect Size Estimation & Weight Prediction Benchmarker.

Amodal_Fruit_Sizing

Fruit detection and measurment using modal and amodal instance segmentation in RGB-D images

fruit_detection_in_LiDAR_pointClouds

Matlab implementation for fruit detection in 3D point clouds acquired with LiDAR sensor Velodyne VLP-16

RGBD_fruit_detection_faster-rcnn.pytorch

apple_size_estimation_in_3D_point_clouds

This project is a matlab implementation for apple size estimation in 3D point clouds. Four different size estimation methods are implemented: largest segment, least squares, MSAC and template matching.

MTLS_point_cloud_generation

Matlab implementation to generate 3D point clouds from data acquired with VLP-16 and GNSS GPS1200+

multitask_RGB-D_FruitDetectionAndSizing

Multitask Deep Neural Network for Fruit Detection and Regresion of Fruit Diameters in RGB-D images (based on Detectron2)

ak_video_analyser

AK_VIDEO_ANALYZER that analyses videos on which to automatically detect apples, estimate their size and predict yield at the plot or per hectare scale using the appropriate simulated algorithms.

ak_frame_extractor

AKFruitData - AK_FRAEX: Tool for extracting frames from video files produced with Azure Kinect cameras. RGB-D camera, Data acquisition, Data extraction, Fruit yield trials, Precision fruticulture.https://doi.org/10.1016/j.softx.2022.101231

ak_sm_recorder

AKFruitData - AK_SM_RECORDER. Azure Kinect single mode recorder. https://pypi.org/project/ak-sm-recorder/

SfM_3D_fruit_detection

Fruit detection and 3D location in photogrammetry-derived 3D point clouds

ak_acquisition_system

AKFruitData - ak_acquisition_system is a software solution for data acquisition in fruit orchards using a sensor system boarded on a terrestrial vehicle. It allows the coordination of computers and sensors through the sending of remote commands via a GUI. https://doi.org/10.1016/j.softx.2022.101231