4.7 Article

A compatible detector based on improved YOLOv5 for hydropower device detection in AR inspection system

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 225, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2023.120065

关键词

Object detection; Wearable AR device; YOLOv5; MobileNetv3; Hydropower plant; Genetic algorithm; Attention mechanism

向作者/读者索取更多资源

Recent advancements in computer vision and augmented reality technology have the potential to enhance human-computer interaction, but their integration is not common in the industry. Efficient object detection models are crucial for accurate object localization in augmented reality devices, but the limited computing capabilities and memory usage of wearable AR devices pose challenges for deploying state-of-the-art detectors.
Recent advancements in computer vision and augmented reality (AR) technology have created a pathway for enhanced human-computer interaction. Despite the potential benefits, the integration of these technologies is not yet common in the industry. Object detection is the foundation of AR systems for inspection, and an efficient object detection model will enable accurate object localization in AR devices. However, the limited computing capabilities and memory usage of wearable AR devices pose a significant challenge to the deployment of state-of-the-art detectors on these devices in real work scenarios. Moreover, as the person wearing the AR device keeps moving, the constantly changing scale of the captured image often leads to significant information loss when feature maps of different scales are fused. To address these challenges, this paper proposes a new compatible detector YOLO-Master based on improved YOLOv5 through deep learning and AR. The paper introduces several improvements, such as using MobileNetv3 as the backbone to reduce memory overhead and computational costs and designing an attention module MCA based on coordinate to reduce information loss. Besides, 20 hyperparameters of YOLOv5 are optimized by the genetic algorithm(GA) before model training, which can help reduces the loss value generated. YOLO-Master was trained and tested on hydropower device images collected from a power plant in the China Southern Power Grid, and the experiments on a self-made dataset demonstrated that the mAP of the proposed method can reach 70.1%, which is 2.9% higher than the baseline of 67.2%. Additionally, the number of parameters of the model was reduced by 66.7%, enabling the model to ensure accuracy while being compatible with real-time performance, meeting the urgent needs of engineering deployment.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据