加强监控系统:将对象,行为和空间信息集成到高级风险评估标题中
Minseong Jeon1, Jaepil Ko2, Kyungjoo Cheoi1
1Department of Computer Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究引入了一种新的监控系统,使用图像标题进行风险评估. 该系统通过分析人工智能生成的场景描述来准确识别安全,危险和危险水平.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 风险管理 风险管理
背景情况:
- 传统的监控系统缺乏细微的风险评估能力.
- 整合人工智能驱动的图像分析可以提高安全性.
- 有效的风险评估需要了解场景背景.
研究的目的:
- 开发一种使用图像标题进行增强风险评估的新型监控系统.
- 创建一个独特的数据集,用于培训和评估监视中的AI模型.
- 在实时监控场景中准确分类风险水平.
主要方法:
- 使用图像标题来生成描述性的场景叙述.
- 开发了一个自定义的数据集,包含[图像-标题-危险评分]条目.
- 微调了BLIP-2模型用于标题生成.
- 用BERT进行标题解释和风险级别评估 (1-7阶段).
主要成果:
- 实现了高准确率:安全性为92.3%,危险性为89.8%,危险性为94.3%.
- 证明了人工智能支持的标题分析在风险评估中的有效性.
- 验证了系统解释复杂场景元素的能力,包括对象,动作和空间背景.
结论:
- 拟议的基于图像标题的监控系统在风险评估方面取得了重大进展.
- 这种新的方法提供了对监视场景的全面了解.
- 这种方法提高了安全监控的准确性和有效性.
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