Results 41 to 50 of about 2,335 (188)

Small Object Detection Method for Bioimages Based on Improved YOLOv8n Model

Integrative Zoology, EarlyView.
This study proposes a small object detection method for bioimages based on an improved YOLOv8n model. Experimental results demonstrate that the proposed approach effectively enhances detection precision, recall, and mAP50, offering a novel solution for the technical challenges in biological microscopy research.
Xiaoyu Li, Chengrui Shang, Xian Hou, Qi Wang, Jiao Wang, Taxing Zhang, Xiangjiang Zhan, Shengkai Pan   +7 more
wiley   +1 more source

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, Volume 142, Issue 4, Page 481-501, August 2026.
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

Network of YOLOv5-LiNet.

, 2023
LiNet backbone including neck of BiFPN, PANet and FPN.
Olarewaju Mubashiru Lawal (14710251)
core   +1 more source

MSFFNet: Multiscale Feature Fusion Network for Small Target Detection in Remote Sensing Images

CAAI Transactions on Intelligence Technology, Volume 11, Issue 2, Page 592-609, April 2026.
ABSTRACT With the advancement of satellite remote sensing technology, object detection based on high‐resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision. Although numerous algorithms have been developed for remote sensing image object detection, they still suffer from challenges such as low ...
Hui Zong, Chenbin Wang, Hao Wang, Xin Cheng, Shangbing Gao, Yunyang Yan   +5 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

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

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

Oral Diseases, Volume 32, Issue 3, Page 791-808, March 2026.
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

Seafloor Sediment Detection with Sidescan Sonar Image Based on YOLO11 and YOLO26

Electronics Letters, Volume 62, Issue 1, January/December 2026.
This paper focuses on the demand of seafloor sediment detection using SSS images. It systematically compares the architectural differences and comprehensive performance of YOLO11 and YOLO26. The two models form differentiated adaptation for different application scenarios. ABSTRACT The study of seafloor geomorphology is the core foundation for decoding
Dandan Liu, Zezhou Jin, Jiajie Chen, Zhiping Xu   +3 more
wiley   +1 more source