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基于图关联注意力特征的校园安全平台跨模态行人检测研究

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为了探索一种新的校园安全监测模式,研究提出基于图关联注意力特征的跨模态行人检测方法.该方法将多模态数据进行融合,并利用注意力机制和掩模引导来增强模型的泛化能力.室内搜索模式下,当排名Rank为 20 时,相比于仅输入完整图像,前景图像和完整图像同时作为输入,可将网络的识别率提升 6.7%.相比于单独的通道注意力模块和空间注意力模块,两者同时使用可将网络的识别率提升 3.1%.改进后的校园安全平台的行人检测的识别率高于 93%,行人轨迹预测的准确率为97.8%.相比于改进前的校园安全平台,改进后的校园安全平台的整体性能满意度评价提升了 31.2%.研究提出的跨模态行人检测方法,提高了校园内行人检测的准确性和可靠性.这有助于保障校园安全,预防和减少安全事故的发生.
Cross modal pedestrian detection on campus security platform based on graph related attention features
In order to explore a new campus safety monitoring mode,a cross modal pedestrian detection method based on graph associated attention features is proposed.This method integrates multimodal data and utilizes attention mechanisms and mask guidance to enhance the model's generalization ability.In indoor search mode,when the Rank is 20,compared to only inputting complete ima-ges,using both foreground and complete images as inputs can improve the recognition rate of the network by 6.7%.Compared to sepa-rate channel attention modules and spatial attention modules,the simultaneous use of both can improve the recognition rate of the net-work by 3.1%.The recognition rate of pedestrian detection on the improved campus safety platform is higher than 93%,and the accu-racy of pedestrian trajectory prediction is 97.8%.Compared to the pre improved campus security platform,the overall performance satisfaction evaluation of the improved campus security platform has increased by 31.2%.The cross modal pedestrian detection method proposed in the study has improved the accuracy and reliability of pedestrian detection on campus.This helps to ensure campus safety,prevent and reduce the occurrence of safety accidents.

mask imageattention mechanismfeature extractioncross modalpedestrian detectioncampus security

黄洪松、梁红娥

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西安思源学院,西安 710038

掩模图像 注意力机制 特征提取 跨模态 行人检测 校园安全

2024

自动化与仪器仪表
重庆工业自动化仪表研究所,重庆市自动化与仪器仪表学会

自动化与仪器仪表

CSTPCD
影响因子:0.327
ISSN:1001-9227
年,卷(期):2024.(11)