Results 61 to 70 of about 7,483 (192)

Pavement crack detection method based on improved YOLOv5

Dianzi Jishu Yingyong
Aiming at the problem that the existing crack detection model is large in size and the detection accuracy is not high, this paper proposes a crack detection method for UAV aerial images based on lightweight network.
Wang Xiangqian, Cheng Gaoli, Hu Peng, Xia Xiaohua   +3 more
doaj   +1 more source

Smart sprayer for weed control using an object detection algorithm (YOLOv5) [PDF]

Journal of Plant Protection Research
Spraying pesticides is one of the most common procedures that is conducted to control pests. However, excessive use of these chemicals inversely affects the surrounding environments including the soil, plants, animals, and the operator itself. Therefore,
Ameer H. Al-Ahmadi, Alaa K. Subr
doaj   +1 more source

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5

PLOS ONE
Solenoid connectors play important role in electronic stability system design, with the features of small size, low cost, fast response time and high reliability. The main production process challenge for solenoid connectors is the accurate detection of defects, which is closely related to safe driving.
Ming Chen, Yuqing Liu, Xing Wei, Zichen Zhang, Oleg Gaidai, Hengshou Sui, Bin Li   +6 more
openaire   +4 more sources

YOLO‐GDCNN: Real‐Time Operating Point Detection for Live Working Robots in the Power Industry

High Voltage, EarlyView.
ABSTRACT In the power industry maintenance, the capability of live working robots to detect and operate with power components in real time is paramount. This paper proposes a cascaded detection framework for real‐time detection of live working operation points, named YOLO‐GDCNN. The framework consists of two parts.
Haoning Zhao, Jiamin Guo, Erbao Dong, Rui Guo, Linkun Zhao, Chaoqun Wang, Xuewen Rong, Yibin Li   +7 more
wiley   +1 more source

Textile and colour defect detection using deep learning methods

Coloration Technology, EarlyView.
Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui, Abdel‐Fattah Seyam, Renzo Shamey   +2 more
wiley   +1 more source

Context Aware Multimodal Fusion YOLOv5 Framework for Pedestrian Detection under IoT Environment [PDF]

Radioengineering
Pedestrian detection based on deep networks has become a research hotspot in the field of computer vision. With the rapid development of the Internet of Things (IoT) and autonomous driving technology, the deployment of pedestrian detection models on ...
Y. Shu, Y. Wang, M. Zhang, J. Yang, Y. Wang, J. Wang, Y. Zhang   +6 more
doaj  

Large coal detection for belt conveyors based on improved YOLOv5

Gong-kuang zidonghua
Oversized coal blocks can easily cause poor coal flow, blockage, and coal stacking when transported on a belt conveyor. However, the differences in appearance and color between large coal blocks and ordinary coal blocks are small, and there are ...
QIN Yulong, CHENG Jiming, REN Yige, WANG Xiaoqing, ZHAO Qing, AN Cuijuan   +5 more
doaj   +1 more source

OralSegNet: An Approach to Early Detection of Oral Disease Using Transfer Learning

Oral Diseases, EarlyView.
ABSTRACT Objective Deep learning‐based segmentation system is proposed that exploits three variants of YOLOv11 architecture, namely YOLOv11n‐seg, YOLOv11s‐seg, and YOLOv11m‐seg for automated detection and localization of the oral disease conditions from photographic intraoral images.
Pranta Barua, Md Rakibul Islam, Mahmud Uz Zaman, Adel Alenazi, Fawaz Alqahtani, Huda Abutayyem, Maher Al Shayeb, Ruba Odeh, Nesrine A. Elsahn, Mohammad Khursheed Alam   +9 more
wiley   +1 more source

DDVC-YOLOv5: An Improved YOLOv5 Model for Road Defect Detection

IEEE Access
Road defect detection is crucial for enhancing traffic safety, optimizing urban management efficiency, and promoting sustainable urban development. Traditional manual detection methods are inefficient and costly, and most deep learning-based road defect detection models lack superior feature extraction capabilities in complex environments.
Shihao Zhong, Chunlin Chen, Wensheng Luo, Siyuan Chen   +3 more
openaire   +2 more sources

Shorebird responses to fine‐scale water level fluctuations and macrofauna biomass in a newly constructed freshwater wetland

Restoration Ecology, EarlyView.
Abstract Introduction Restoration of marine and freshwater wetlands for shorebirds is essential for the recovery of their declining populations. An ongoing approach is to restore shorebird habitats by large‐scale engineering, expecting the return of birds once suitable abiotic conditions are (re)established.
Lars Ursem, Elisabeth S. Bakker, Hui Jin, Casper H. A. van Leeuwen   +3 more
wiley   +1 more source