AHLNet: Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations
文献类型：期刊论文
| 作者 | Mengle Peng1,2; Xiaoyong Jiang1; Langyue Huang1,2; Zhongyi Li1,2; Haiteng Wu2; Xiaotang Geng2 |
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2024 |
| 卷号 | 21期号:5页码:983-992 |
| 关键词 | Adaptive multihead structure lightweight feature pyramid substation feature imbalance multiobject detection |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-023-1427-7 |
| 英文摘要 | With the increasing demand for power in society, there is much live equipment in substations, and the safety and standardization of live working of workers are facing challenges. Aiming at these problems of scene complexity and object diversity in the real time detection of the live working safety of substation workers, an adaptive multihead structure and lightweight feature pyramid-based network (AHLNet) is proposed in this study, which is based on YOLOV3. First, we take AH-Darknet53 as the backbone network of YOLOV3, which can introduce an adaptive multihead (AMH) structure, reduce the number of network parameters, and improve the feature extraction ability of the backbone network. Second, to reduce the number of convolution layers of the deeper feature map, a lightweight feature pyramid network (LFPN) is proposed, which can perform feature fusion in advance to alleviate the problem of feature imbalance and gradient disappearance. Finally, the proposed AHLNet is evaluated on the datasets of 16 categories of substation safety operation scenarios, and the average prediction accuracy MAP50 reaches 82.10%. Compared with YOLOV3, MAP50 is increased by 2.43%, and the number of parameters is 90 M, which is only 38% of the number of parameters of YOLOV3. In addition, the detection speed is basically the same as that of YOLOV3, which can meet the real-time and accurate detection requirements for the safe operation of substation staff. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59426]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Zhejiang University of Science and Technology, Hangzhou 310023, China 2.Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot, Hangzhou 311100, China |
| 推荐引用方式 GB/T 7714 | Mengle Peng, Xiaoyong Jiang, Langyue Huang,et al. AHLNet: Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations[J]. Machine Intelligence Research,2024,21(5):983-992. |
| APA | Mengle Peng, Xiaoyong Jiang, Langyue Huang,Zhongyi Li, Haiteng Wu,& Xiaotang Geng.(2024).AHLNet: Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations.Machine Intelligence Research,21(5),983-992. |
| MLA | Mengle Peng,et al."AHLNet: Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations".Machine Intelligence Research 21.5(2024):983-992. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。