Gesture Recognition Controls Image Style Transfer Based on Improved YOLOV5s Algorithm

Xie, Jiangfan; Jin, Huilong; Wen, Tian; Du, Ruiyan

doi:10.1007/978-3-030-98002-3_15

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Dima et al 20 used YOLOv5 algorithm to detect and recognize American sign language. Xie et al 21 proposed an improved gesture recognition algorithm for YOLOv5s and used the results of gesture recognition for interaction experiments with a computer. Liao et al 22 replaced the front-end feature extraction network of SSD with MobileNet, and the back-end network still used the detection network of SSD.…”

Section: Deep Learning-based Recognitionmentioning

confidence: 99%

An overview of gesture recognition

et al. 2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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With the development of artificial intelligence and human-computer interaction technology, gesture has been widely used in intelligent vehicles, human-computer interaction, virtual reality, and other fields as an important way of communication in people’s daily life. However, there are few review studies that comprehensively evaluate and summarize gesture recognition in recent years. To address this situation, this paper classifies and summarizes the currently used gesture recognition methods. Firstly, the current status of research on static gestures is investigated, followed by an analysis of the progress of research on dynamic gesture recognition, then the commonly used gesture datasets are introduced and their respective characteristics are described, finally, the current challenges and future development trends of gesture recognition are analyzed.

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Section: Deep Learning-based Recognitionmentioning

confidence: 99%

An overview of gesture recognition

et al. 2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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A lightweight road crack detection algorithm based on improved YOLOv7 model

He,

Wang,

Wang

et al. 2024

SIViP

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Automatic Detection of Bridge Surface Crack Using Improved YOLOv5s

Yang¹,

Yang²,

Tie-jun³

et al. 2022

Int. J. Patt. Recogn. Artif. Intell.

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Bridge crack detection is a key task in the structural health monitoring of Civil Engineering. In the traditional bridge crack detection methods, there exist some problems such as high cost, low speed, and complex structure. This paper developed a bridge surface crack detection system based on improved YOLOv5s. The GhostBottleneck module was employed to replace the classic C3 module of the YOLOv5s backbone network, meanwhile the channel attention module namely ECA-Net was also added to the network, which not only reduced the amount of calculation, but also enhanced the ability of the network in extracting cross-channel information features. The adaptive spatial feature fusion (ASFF) was introduced to address the conflict problem caused by the inconsistency of feature scale in the network feature fusion stage, and the transfer learning was utilized to train the network. The experimental results showed that the improved YOLOv5s performed better than Faster R-CNN, SSD, YOLOv3, and YOLOv5s, with the Precision of 93.6%, Recall of 95.4%, and mAP of 98.4%. Further, the improved YOLOv5s was deployed in PyQt5 to realize the real-time detection of bridge cracks. This research showed that the proposed model not only provides a novel solution for bridge surface crack detection, but also has certain industrial application value.

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Gesture Recognition Controls Image Style Transfer Based on Improved YOLOV5s Algorithm

Cited by 3 publications

References 11 publications

An overview of gesture recognition

An overview of gesture recognition

A lightweight road crack detection algorithm based on improved YOLOv7 model

Automatic Detection of Bridge Surface Crack Using Improved YOLOv5s

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