断路器
功率(物理)
计算机科学
对象(语法)
目标检测
实时计算
算法
电子工程
工程类
电气工程
人工智能
模式识别(心理学)
物理
量子力学
作者
Riben Shu,Lihua Chen,Lumei Su,Tianyou Li,Fan Yin
出处
期刊:Electronics
[Multidisciplinary Digital Publishing Institute]
日期:2024-10-07
卷期号:13 (19): 3949-3949
标识
DOI:10.3390/electronics13193949
摘要
In the scenario of power system monitoring, detecting the operating status of circuit breakers is often inaccurate due to variable object scales and background interference. This paper introduces DLCH-YOLO, an object detection algorithm aimed at identifying the operating status of circuit breakers. Firstly, we propose a novel C2f_DLKA module based on Deformable Large Kernel Attention. This module adapts to objects of varying scales within a large receptive field, thereby more effectively extracting multi-scale features. Secondly, we propose a Semantic Screening Feature Pyramid Network designed to fuse multi-scale features. By filtering low-level semantic information, it effectively suppresses background interference to enhance localization accuracy. Finally, the feature extraction network incorporates Generalized-Sparse Convolution, which combines depth-wise separable convolution and channel mixing operations, reducing computational load. The DLCH-YOLO algorithm achieved a 91.8% mAP on our self-built power equipment dataset, representing a 4.7% improvement over the baseline network Yolov8. With its superior detection accuracy and real-time performance, DLCH-YOLO outperforms mainstream detection algorithms. This algorithm provides an efficient and viable solution for circuit breaker status detection.
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