Results 61 to 70 of about 2,016 (190)

EAFE‐OSOD: Efficient Attention Fusion Encoder for Open‐Set Object Detection in UAV Applications

IET Image Processing, Volume 20, Issue 1, January/December 2026.
We propose EAFE‐OSOD, an efficient end‐to‐end framework for open‐set object detection in UAV applications that simultaneously addresses small object detection, computational efficiency, and novel category recognition. The method integrates class‐agnostic mask embedding and CLIP text encoder with streamlined multi‐scale fusion architectures to enable ...
Zicheng Wang, Zhenxiang Pan, Zhiyu Cai, Hui Zhou, Yu Tang, Yingchi Mao   +5 more
wiley   +1 more source

A High-Quality Instance-Segmentation Network for Floating-Algae Detection Using RGB Images

, 2022
Floating-algae detection plays an important role in marine-pollution monitoring. The surveillance cameras on ships and shores provide a powerful way of monitoring floating macroalgae. However, the previous methods cannot effectively solve the challenging
Yibo Zou, Yan Ge, Lei Wang, Linlin Zhao, Xiaoliang Wang, Ke Chen   +5 more
core   +1 more source

A Highway Litter Detection Method Based on Multi‐scale Feature Fusion and Dynamic Feature Enhancement

IET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.
To address missed detections and false positives in highway litter detection caused by small target sizes and feature degradation, this study proposes a novel framework integrating multi‐scale feature fusion and dynamic feature enhancement mechanisms, which includes a contextual anchor attention module, an improved spatial pyramid pooling module, a ...
Changlu Guo, Yecai Guo, Songbin Li
wiley   +1 more source

REMW‐YOLO: A Balanced Dense Pedestrian Detection Model With Efficient Feature Extraction and Fusion

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
Dense pedestrian detection plays a crucial role in intelligent security and video analysis, yet it faces challenges such as occlusion, scale variations, and difficulties in feature extraction. To address these, this study proposes REMW‐YOLO, an efficient model built on YOLO11n that balances performance and computational efficiency. To tackle occlusion,
Hefeng Yu, Linan Zu, Xiaona Pan, Chao Tang, Ming Liang Jin, Pingping Meng, Kallol Mohanta   +6 more
wiley   +1 more source

Transmission Line Defect Detection in Low‐Light Environments Using Image Adversarial Synthesis and the LCDD‐YOLO Algorithm

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
To address the requirements for high‐precision detection of transmission line defects by inspection drones in low‐light environments such as cloudy days and to overcome the problem of significant accuracy degradation in current defect detection algorithms under low‐light conditions, this paper uses YOLOv8 as the baseline algorithm.
Yuxin Zhu, Shaotong Pei, Floriano Cuccureddu   +2 more
wiley   +1 more source

The structure of the BiFPN.

An essential industrial application is the examination of surface flaws in hot-rolled steel strips. While automatic visual inspection tools must meet strict real-time performance criteria for inspecting hot-rolled steel strips, their capabilities are ...
Haoyue Huang (3450605), Qiuyan Wang (237527), Haibing Dong (17846963)   +2 more
core   +1 more source

Dataset Construction and Real‐Time Monitoring for Developing an Intelligent Drowning Warning Algorithm by the CamTra System

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
To solve the problem of sparse images in real‐world drowning datasets, this study aims to create an intelligent system that can generate a large number of drowning datasets by optimizing AI image generation algorithms. The system will gradually be used to make up for the shortage of rare real‐world drowning datasets based on the CamTra (camera tracking)
Bing Bai, Chen Chuan Yu, Yan Wu, Sinuo Hou, Wang Qin, Litao Zhou, Xiaozheng Li, Richard Murray   +7 more
wiley   +1 more source

Improved Tomato Leaf Disease Recognition Based on the YOLOv5m with Various Soft Attention Module Combinations

Agriculture
To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. Early diagnosis and treatment are necessary to cure and control illnesses and ensure tomato output and quality. The YOLOv5m was
Yong-Suk Lee, Maheshkumar Prakash Patil, Jeong Gyu Kim, Seong Seok Choi, Yong Bae Seo, Gun-Do Kim   +5 more
doaj   +1 more source

An Improved YOLOv11 Method With Mamba Module for Multiscale Tea Disease Detection

Journal of Electrical and Computer Engineering, Volume 2026, Issue 1, 2026.
Deep network–based models have been widely applied in smart agriculture, including tea disease detection tasks. However, the inference of existing deep models requires a large amount of computing resources and storage space, making it difficult to effectively deploy on mobile devices.
Yun Pan, Deepali
wiley   +1 more source

OPTIMASI DETEKSI PENYAKIT DAUN TEH BERBASIS YOLOV8 MENGGUNAKAN CONVOLUTIONAL BLOCK ATTENTION MODULE (CBAM) DAN BIDIRECTIONAL FEATURE PYRAMID NETWORK (BiFPN) [PDF]

Deteksi dini penyakit daun teh penting untuk menjaga kualitas produksi, namun proses ini menghadapi tantangan berupa keragaman bentuk dan ukuran gejala serta latar belakang citra yang kompleks. Penelitian ini mengoptimalkan YOLOv8 dengan mengintegrasikan
Armijantoro, Gilang Rahmadhan
core