Zobrazeno 1 - 10
of 180
pro vyhledávání: '"IF-CIOU"'
Autor:
Jifeng LU, Chaoyu YANG
Publikováno v:
Meikuang Anquan, Vol 55, Iss 11, Pp 250-256 (2024)
A lightweight miner illegal behavior intelligent detection algorithm based on M3CFC-YOLOv7-tiny is proposed to address the three challenges of deploying lightweight equipment in coal mines, low recognition precision in complex environment, and imbala
Externí odkaz:
https://doaj.org/article/526b01653c0c4b158e3d0af62737c826
Autor:
Henghui Mo, Linjing Wei
Publikováno v:
Ecological Informatics, Vol 85, Iss , Pp 102912- (2025)
Accurate identification of Tomato Yellow Leaf Curl Virus (TYLCV) is essential for ensuring the sustainability of tomato cultivation, as tomatoes are a globally important commercial crop. TYLCV manifests as stunted growth, yellowing, and curling of le
Externí odkaz:
https://doaj.org/article/9415513b30f1456c932c97f58c453ebd
Publikováno v:
Frontiers in Neurorobotics, Vol 18 (2024)
Object detection plays a crucial role in robotic vision, focusing on accurately identifying and localizing objects within images. However, many existing methods encounter limitations, particularly when it comes to effectively implementing a one-to-ma
Externí odkaz:
https://doaj.org/article/b8316a0f411346bab76d1fa417e519c3
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
The detection of apple leaf diseases plays a crucial role in ensuring crop health and yield. However, due to variations in lighting and shadow, as well as the complex relationships between perceptual fields and target scales, current detection method
Externí odkaz:
https://doaj.org/article/c103107fd0c94278ae891e72816500ce
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
In the current agricultural landscape, a significant portion of tomato plants suffer from leaf diseases, posing a major challenge to manual detection due to the task’s extensive scope. Existing detection algorithms struggle to balance speed with ac
Externí odkaz:
https://doaj.org/article/7cdf22eaf310407f94a43f9d2396031d
Autor:
Liang, Min a, b, 1, Zhang, Yuchen a, 1, Zhou, Jian c, Shi, Fengcheng c, Wang, Zhiqiang d, Lin, Yu a, Zhang, Liang b, ⁎, Liu, Yaxi a, ⁎
Publikováno v:
In Smart Agricultural Technology March 2025 10
Publikováno v:
IEEE Access, Vol 12, Pp 69633-69641 (2024)
Aiming at the problem of low visibility of underwater environment, which leads to the leakage of small target detection and low accuracy, this paper proposes an improved algorithm USSTD-YOLOv8n (Underwater small-size target detection YOLOv8n) based o
Externí odkaz:
https://doaj.org/article/36ef854b53004bd3a54d0332a4a2f3d8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4304-4319 (2024)
In light of rapid economic and urban growth, the proliferation of structures including transmission towers, signal poles, and wind generators has become evident. Consequently, precise object detection of these structures emerges as a pivotal approach
Externí odkaz:
https://doaj.org/article/43571df6a0b24cac9c118a031df78f4c
Publikováno v:
Agronomy, Vol 14, Iss 11, p 2557 (2024)
This study addresses the issue of inaccurate and error-prone grading judgments in luffa plug seedlings. A new Seg-FL seedling segmentation model is proposed as an extension of the YOLOv5s-Seg model. The small leaves of early-stage luffa seedlings are
Externí odkaz:
https://doaj.org/article/05ecc4a72bf2408b8e77323716a2d383
Publikováno v:
Emergency Management Science and Technology, Vol 3, Iss 1, Pp 1-13 (2023)
Computer vision technology has broad application prospects in the field of intelligent fire detection, which has the benefits of accuracy, timeliness, visibility, adjustability, and multi-scene adaptability. Traditional computer vision algorithm flaw
Externí odkaz:
https://doaj.org/article/746c8c10629a4f158ccb79e46c72db84