Results 31 to 40 of about 2,016 (190)

A traffic sign detection model based on coordinate attention - bidirectional feature pyramid network

Shenzhen Daxue xuebao. Ligong ban, 2023
In the field of autonomous driving, the correct detection of traffic signs can provide important information for environmental perception. To address the low recognition rate and misdetection and missed detection issues of various traffic signs, we ...
LANG Binke, LÜ Bin, WU Jianqing, WU Ruinian   +3 more
doaj   +1 more source

Development of Face and Landmark Detection using EfficientNetV2 and BiFPN

, 2022
In this paper, we develop improved face and landmark detection algorithm using EfficientNetV2 as backbone and BiFPN as multi-scale feature extractor. EfficientNetV2 are a new family of convolutional networks that have faster training speed and better ...
Kim, Hyunduk, Lee, Sang-Heon, Sohn, Myoung-Kyu   +2 more
core   +1 more source

Protective Equipment Wearing Detection Algorithm in Construction Scenarios Based on YOLOv8n [PDF]

Zhengzhou Daxue xuebao. Gongxue ban
In view of the problems of protective equipment detection, such as information interference, uneven illumination and occlusion in the construction scene, a lightweight algorithm with improved YOLOv8n was proposed, which was called YOLO-LA .
LI Jun, ZHOU Keyu, ZOU Jun, ZENG Wenbing   +3 more
doaj   +1 more source

Traffic Sign Detection Based on Lightweight YOLOv5 [PDF]

Zhengzhou Daxue xuebao. Gongxue ban
In order to improve the detection speed of road traffic signs, an improved model based on lightweight YOLOv5 was proposed. Firstly, Ghost convolution and depthwise convolution were used to build a new Bottleneck, which could reduce the amount of ...
ZHANG Zhen, WANG Xiaojie, JIN Zhihua, MA Jijun   +3 more
doaj   +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

Crop Pest Target Detection Algorithm in Complex Scenes:YOLOv8-Extend

智慧农业
ObjectiveIt is of great significance to improve the efficiency and accuracy of crop pest detection in complex natural environments, and to change the current reliance on expert manual identification in the agricultural production process.
ZHANG Ronghua, BAI Xue, FAN Jiangchuan
doaj   +1 more source

A coal mine underground drill pipes counting method based on improved YOLOv8n

Gong-kuang zidonghua
In order to improve the efficiency and precision of underground drill pipe counting in coal mines, a coal mine underground drill pipe counting method based on the improved YOLOv8n model is proposed.
JIANG Yuanyuan, LIU Songbo
doaj   +1 more source

Severity Grading Model for Camellia Oleifera Anthracnose Infection Based on Improved YOLACT

智慧农业
ObjectiveCamellia oleifera is one of the four major woody oil plants in the world. Diseases is a significant factor leading to the decline in quality of Camellia oleifera and the financial loss of farmers.
NIE Ganggang, RAO Honghui, LI Zefeng, LIU Muhua   +3 more
doaj   +1 more source

Helmet detection method based on improved YOLOv5

工程科学学报
To address the challenge of low detection accuracy in existing safety helmet detection algorithms, particularly in scenarios with small targets, dense environments, and complex surroundings like construction sites, tunnels, and coal mines, we introduce ...
Gongyu HOU, Qinhuang CHEN, Zhenhua YANG, Youwen ZHANG, Danyang ZHANG, Haoxiang LI   +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