ACD-Net:用于复杂船舶场景的异常船员检测网络
Zhengbao Li1, Heng Zhang1, Ding Gao1
1College of Ocean Science and Engineering, Shandong University of Science and Technology, No. 579 Qianwan Port Road, Huangdao District, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究介绍了ACD-Net,这是一种用于检测船舶船员异常行为的AI系统. 它在实时检测和识别方面实现了高精度,提高了海上安全.
科学领域:
- 海上安全的航行.
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 不正常的船员行为是导致船舶事故的重要因素.
- 当前的识别算法在复杂的,开放的船舶环境中扎.
- 实时检测和识别异常行为对于安全至关重要.
研究的目的:
- 为复杂的船舶场景提出一个先进的异常船员检测网络 (ACD-Net).
- 为了增强对异常船员行为的实时检测和识别能力.
- 提高算法在开放和动态的船舶环境中的性能.
主要方法:
- 开发了一种改进的YOLOv5s模型 (YOLO-TRCA),配备了变压器和CBAM,用于增强特征提取.
- 集成了一个使用CenterFace,Filter和ArcFace进行实时面部识别的机组面部算法 (CFA).
- 采用了两阶段的方法来检测和识别异常的船员.
主要成果:
- 在检测异常行为方面,ACD-Net的准确率达到了92.3%.
- 实现了69.6%的身份识别匹配率.
- 在1080P分辨率下每处理数据不到39.5毫秒.
结论:
- 在复杂的海上环境中,ACD-Net显著改善了异常船员的检测和识别.
- 提出的方法提高了准确性,并减少了计算开销.
- 该系统提供了一个可行的解决方案,用于实时监控和提高船舶上的安全.
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