Results 141 to 150 of about 14,166 (237)

The network structure of improved YOLOv5 model.

Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity ...
Danya Zhang (15309566), Xuesong Huang (18468785), Ligang Qu (18468782), Zeng Chen (6910307)   +3 more
core   +1 more source

Pedestrian detection method based on improved YOLOv5

With the development of autonomous vehicles and intelligent transportation, more accurate detection of pedestrians. However, pedestrian detection suffers from occlusion and small target.
Shangtao You, Kai Zhu, Zhenchao Gu
core   +1 more source

The improved YOLOv5 model training results.

Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity ...
Danya Zhang (15309566), Xuesong Huang (18468785), Ligang Qu (18468782), Zeng Chen (6910307)   +3 more
core   +1 more source

AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images. [PDF]

Transl Vis Sci Technol
Maehara H, Ueno Y, Oda M, Ito Y, Kitaguchi Y, Miyazaki D, Nejima R, Inomata T, Kato N, Chikama TI, Ominato J, Yunoki T, Tsubota K, Suzutani M, Mori K, Yamaguchi T, Oshika T.   +16 more
europepmc   +1 more source

An intelligent approach for automated vehicle damage classification. [PDF]

Sci Rep
Ouf S, Lotfy B, Ahmed S.
europepmc   +1 more source

Object Detection Using YOLOv5 and OpenCV

Object detection is one of the main tasks in computer vision, aimed at recognizing and localizing objects in images or videos. In this study, we utilize the YOLOv5 model, which is well known for its efficiency in realtime object detection.
Subagja , Mifta, Rahman, Ben
core   +1 more source

Performance evaluation of deep learning based YOLOv5 and YOLOv8 models for real time breast cancer detection in mammographic images. [PDF]

Discov Oncol
Sahoo N, Kar A, Khemchandani V.
europepmc   +1 more source

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Underwater light attenuation leads to decreased image contrast. This reduction in contrast subsequently decreases target visibility. Additionally, marine target detection is challenging due to multi-scale problems from varying target-to-device distances,
Huang Shen-hao, W.E.I. Lian-suo, Ma Long-yu   +2 more
core   +1 more source

NL-YOLOv5: a model with a larger receptive field and the ability to globally acquire features. [PDF]

Front Neurorobot
Li Z, Liu J, Zhuo Z, Chen L, Zeng X, Li D, Zhou Y, Huang C.   +7 more
europepmc   +1 more source

SAB-YOLOv5: An Improved YOLOv5 Model for Permanent Magnetic Ferrite Magnet Rotor Detection

Surface defects on the permanent magnetic ferrite magnet rotor are the primary cause for the decline in performance and safety hazards in permanent magnet motors. Machine-vision methods offer the possibility to identify defects automatically. In response
Qi Li, Wenhua Jiao, Bo Yu, Shiyang Zhang, Yongjun Zhu   +4 more
core   +1 more source