Results 21 to 30 of about 12,214 (239)
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
, 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
core +1 more source
IVP-YOLOv5: an intelligent vehicle-pedestrian detection method based on YOLOv5s
Connection Science, 2023 Computer vision is now vital in intelligent vehicle environment perception systems. However, real-time small-scale pedestrian detection in intelligent vehicle environment perception systems is still needs to be improved. This paper proposes an intelligent vehicle-pedestrian detection method based on YOLOv5s, named IVP-YOLOv5, to use in vehicle ...
Yang Sun +5 more
openaire +2 more sources
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
core +1 more source
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
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
Risk assessment method for external breakage of overhead lines in mining areas
Gong-kuang zidonghua, 2023 The operating environment of overhead lines in mining areas is harsh. The lines are easily affected by external factors, leading to line breakage. It is necessary to accurately evaluate the risk level of external breakage of overhead lines in mining ...
LIU Zhenguo +3 more
doaj +1 more source
bird-feeder/BirdFSD-YOLOv5: BirdFSD-YOLOv5-v1.0.0-alpha.6
, 2022 Training start time: 2022-06-14T16:25:02 Training duration: 04:22:31 W&B run URL: https://wandb.ai/biodiv/train/runs/y31qiwv2 W&B run ID: y31qiwv2 W&B run name: proud-glade-102 W&B run path: biodiv/train/y31qiwv2 Number of classes: 15 Dataset name ...
Alyetama, DeepSource Bot
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)
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