Results 31 to 40 of about 1,461 (155)

Research on multi-objective detection method for incomplete information in coal mine underground

Meitan kexue jishu
Underground target detection technology in coal mines is an indispensable component of constructing a smart mine, providing real-time monitoring and recognition capabilities.
Lin SUN, Sheng CHEN, Xulong YAO, Yanbo ZHANG, Zhigang TAO, Peng LIANG   +5 more
doaj   +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

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

Application of YOLOv8 Architecture Optimized based on BiFPN in Leather Defect Recognition

Pige Kexue Yu Gongcheng
Traditional image processing methods are difficult to effectively deal with complex backgrounds and defects with different scales. This paper proposed a YOLOv8 architecture optimization strategy that integrates Bidirectional Feature Pyramid Network ...
Hao TANG, Faming CHEN, Zhipeng FENG, Lingzhi HE   +3 more
doaj   +1 more source

Target detection for remote sensing based on the enhanced YOLOv4 with improved BiFPN

The Egyptian Journal of Remote Sensing and Space Science, 2023
To solve problems for false detection, inadequate regression performance of anchor frames, and the inability to detect small targets in traditional multiscale target detection methods based on YOLOv4, we propose a novel target detection framework named as Enhanced YOLOv4.
Fuzhen Zhu, Yuying Wang, Jingyi Cui, Guoxin Liu, Huiling Li   +4 more
openaire   +2 more sources

SW‐YOLO: An Optimized YOLOv8 Architecture With ConvNeXt for Real‐Time and Accurate Substation Wiring Inspection

Engineering Reports, Volume 8, Issue 3, March 2026.
The study uses affine transformation, contrast enhancement, and mosaic masking for image enhancement and introduces the Convolutional NeXt module in YOLOv8 based on masked self‐encoder and response normalization, along with improvements to the convolutional block attention module.
Yu Lei, Zhihao Liang, Jiayun Huang
wiley   +1 more source

AI‐Driven Deep Learning Framework for Detecting Subtle Surface Defects on Wind Turbine Blades

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT Wind turbine blade surface defect detection is of great significance in ensuring the safety and operational efficiency of wind power systems. However, accurately detecting subtle and small‐scale defects remains challenging under complex imaging conditions.
Shoutu Li, Jiawei Pan, Ling Liu, Jin Zhao, Nianxi Yue, Zhen Wu   +5 more
wiley   +1 more source

FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection

Sensors, 2022
UAV-based object detection has recently attracted a lot of attention due to its diverse applications. Most of the existing convolution neural network based object detection models can perform well in common object detection cases.
Wenyu Xu, Chaofan Zhang, Qi Wang, Pangda Dai   +3 more
doaj   +1 more source

AHD‐YOLO: An Adaptive Hybrid Dynamic Network for Building Damage Detection

IET Image Processing, Volume 20, Issue 1, January/December 2026.
To address the issues of limited detection accuracy and high computational resource consumption in current deep learning‐based building damage detection, we propose a novel framework, AHD YOLO, built upon YOLOv11. AHD YOLO achieves an optimal balance between detection performance and computational resource efficiency, demonstrating strong potential for
Min Li, Tao Xu, Yinping Jiang, Peiyong Ji, Jingqi Hu, Wenlong Lui, Xuejian Ji, Ruiqiang Guo   +7 more
wiley   +1 more source

GS-BiFPN-YOLO: A Lightweight and Efficient Method for Segmenting Cotton Leaves in the Field

Agriculture
Instance segmentation of cotton leaves in complex field environments presents challenges including low accuracy, high computational complexity, and costly data annotation. This paper presents GS-BiFPN-YOLO, a lightweight instance segmentation method that
Weiqing Wu, Liping Chen
doaj   +1 more source