Results 21 to 30 of about 14,166 (237)
YOLOv5-plum model detection effect.
, 2023 Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant ...
Qianqian Wu (818952) +4 more
core +1 more source
Alyetama/label-studio-yolov5: v1.0.0-alpha
, 2023 Prepare and train a YOLOv5 model from a Label Studio ...
Mohammad Alyetama
core +1 more source
Automatic Number Plate Recognition System for Indian Number Plates using Machine Learning Techniques [PDF]
ITM Web of Conferences, 2022 India being a country where the population is above 1.3 billion where each person has at least one car of his/her use. Considering this, the number of cars driven on the roads of India must be greater than the population of the people in the country ...
Hajare Gayatri +3 more
doaj +1 more source
, 2021 This repository has been developed to provide a simplified YOLOv5 Object Detection workflow using georefernced images, from training to GIS mapping of results.
Giacomo Nodjoumi
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Real-Time YOLO Based Ship Detection Using Enriched Dataset [PDF]
Iranian Journal of Electrical and Electronic Engineering, 2023 We propose a real-time Yolov5 based deep convolutional neural network for detecting ships in the video. We begin with two famous publicly available SeaShip datasets each having around 9,000 images.
A. Ataee, S. J. Kazemitabar
doaj
bird-feeder/BirdFSD-YOLOv5: BirdFSD-YOLOv5-v1.0.0-alpha.5
, 2022 Training start time: 2022-05-26T19:36:04 Training duration: 10:59:45 W&B run URL: https://wandb.ai/biodiv/train/runs/2mdvvovg W&B run ID: 2mdvvovg W&B run name: graceful-jazz-88 W&B run path: biodiv/train/2mdvvovg Number of classes: 14 Classes ```JSON {
Alyetama
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AP comparison between YOLOv5-plum and YOLOv5.
, 2023 Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant ...
Qianqian Wu (818952) +4 more
core +1 more source
Image results of YOLOv5-LiNetBiFPN.
, 2023 To meet the goals of computer vision-based understanding of images adopted in agriculture for improved fruit production, it is expected of a recognition model to be robust against complex and changeable environment, fast, accurate and lightweight for a ...
Olarewaju Mubashiru Lawal (14710251)
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Autonomous inspection of steel pipe weld lines implementing frequency analysis combined with YOLOv5 [PDF]
, 2023 : In this research, autonomous inspection of steel pipe weld lines for a single class of defects is done using a frequency analysis combined YOLOv5 (You Only Look Once) model.
core +1 more source
Deep Learning Based Steel Pipe Weld Defect Detection
Applied Artificial Intelligence, 2021 Steel pipes are widely used in high-risk and high-pressure scenarios such as oil, chemical, natural gas, shale gas, etc. If there is some defect in steel pipes, it will lead to serious adverse consequences.
Dingming Yang +3 more
doaj +1 more source