Results 31 to 40 of about 14,166 (237)
, 2021 shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16).
chen
core +1 more source
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
Image results of YOLOv5-LiNetC.
, 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
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
Analysis of player tracking data extracted from football match feed
Revista Română de Informatică și Automatică, 2023 Data analytics and AI have become extremely relevant in today’s football landscape. Data is benefiting clubs in gaining a competitive advantage on and off the field by empowering them to harvest information for improving player performance, decreasing ...
Swetha SASEENDRAN +3 more
doaj +1 more source
YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection
CoRR, 2022 In response to the situation that the conventional bridge crack manual detection method has a large amount of human and material resources wasted, this study is aimed to propose a light-weighted, high-precision, deep learning-based bridge apparent crack recognition model that can be deployed in mobile devices' scenarios.
openaire +2 more sources
Image results of YOLOv5-LiNet.
, 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
DS-YOLOv5: A real-time detection and recognition model for helmet wearing
工程科学学报, 2023 Automatic detection and recognition of safety helmet wearing based on video analysis is important to ensure production safety. It is inefficient to supervise whether workers wear safety helmets by manual means.
Peirui BAI +6 more
doaj +1 more source
Image results of YOLOv5-LiNetFPN.
, 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
Image results of YOLOv5-ShuffleNetv2.
, 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