MCI-GLA Plug-In Suitable for YOLO Series Models for Transmission Line Insulator Defect Detection

Wang, Yaru; Song, Xiaoke; Feng, Lilong; Zhai, Yongjie; Zhao, Zhenbing; Zhang, Shiyin; Wang, Qianming

doi:10.1109/tim.2024.3385817

Cited by 1 publication

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experimental part, classical methods such as YOLO3, SSD, Faster RCNN, and TPH-YOLO [24] were selected to compare their performance with that of the proposed method, and the results thus obtained are shown in Table III.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

CCL-YOLO: Catenary Components Location Based on YOLO and Gather-Distribute Mechanism

Xu,

Fei,

Zhao

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The catenary is an important part of the railway power supply system, and the good health of its key components is an important condition for the operation of high-speed trains. Due to the complex composition of catenary components and the large size difference between components, it is very difficult to implement the positioning and detection of all components. It severely limits the subsequent failure analysis of catenary components. Aiming at the problem of the difficult positioning of multiple components, this paper proposes a catenary complex component locating method based on the improved YOLO algorithm named CCL-YOLO. Firstly, based on the YOLO baseline method, a framework for locating complex components of railway catenary is proposed, which achieves the accurate locating of 40 catenary components and paves the way for subsequent fault detection. Furthermore, a novel "Gather-and Distribute" mechanism is introduced, which improves the original feature fusion method and its global feature fusion capability and effectively improves the positioning accuracy of complex catenary components. In addition, the Wasserstein distance loss is also applied to improve the detection capability of small objects. Finally, the comprehensive experimental results show that the proposed method for locating complex components of railway catenary can achieve a mean intersection over union of 94.1% for 40 components of different sizes. Meanwhile, it reduces the number of parameters and the complexity of the algorithm to a certain extent, thus paving the way for subsequent fault detection, which is of important application value.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In general, YOLO, as a single-stage object detection algorithm, can achieve faster detection speed with good generalization ability, while ensuring higher detection accuracy [24]. Therefore, we use YOLO as the benchmark.…”

Section: Architecture Of Yolov5mentioning

confidence: 99%