{"title":"YOLOv11s- cd:一种改进的YOLOv11s悬链线滴管故障检测方法","authors":"Cheng Luo;Hao Tang;Shuning Li;Guohao Wan;Weirong Chen;Jinfa Guan","doi":"10.1109/TIM.2025.3604118","DOIUrl":null,"url":null,"abstract":"The catenary dropper (CD) fault detection is an important technical means to ensure the train current collection quality and operational safety. The existing you only look once (YOLO) detection algorithms need improvement in terms of accuracy, especially in the detection of small objects. To address the problem, this article proposes a CD fault detection model based on improved YOLOv11s, named YOLOv11s-CD. First, a four-detection head structure DASFFHead is designed to achieve multiscale feature fusion by integrating a small object detection layer into the neck network and combining a dynamic adaptive spatial feature fusion (DASFF) module. Subsequently, the squeeze–excitation and attention module (SEAM) attention mechanism is embedded in the neck network layer to extract more small object features in occluded areas. In addition, combining the InnerIoU and CIoU methods, the InnerCIoU loss function is designed to enhance the small object detection ability. Finally, the effectiveness and accuracy of the proposed model are validated on the dataset, which is processed by the optimized contrast-limited adaptive histogram equalization (CLAHE) algorithm to enhance the contrast and clarity of the small object defects. Experimental results show that the proposed YOLOv11s-CD has superior performance compared with several other YOLO algorithms, whose mAP@0.5 has increased to 92.3% and AP of small object detection has significantly increased to 91.3%.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"YOLOv11s-CD: An Improved YOLOv11s Method for Catenary Dropper Fault Detection\",\"authors\":\"Cheng Luo;Hao Tang;Shuning Li;Guohao Wan;Weirong Chen;Jinfa Guan\",\"doi\":\"10.1109/TIM.2025.3604118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The catenary dropper (CD) fault detection is an important technical means to ensure the train current collection quality and operational safety. The existing you only look once (YOLO) detection algorithms need improvement in terms of accuracy, especially in the detection of small objects. To address the problem, this article proposes a CD fault detection model based on improved YOLOv11s, named YOLOv11s-CD. First, a four-detection head structure DASFFHead is designed to achieve multiscale feature fusion by integrating a small object detection layer into the neck network and combining a dynamic adaptive spatial feature fusion (DASFF) module. Subsequently, the squeeze–excitation and attention module (SEAM) attention mechanism is embedded in the neck network layer to extract more small object features in occluded areas. In addition, combining the InnerIoU and CIoU methods, the InnerCIoU loss function is designed to enhance the small object detection ability. Finally, the effectiveness and accuracy of the proposed model are validated on the dataset, which is processed by the optimized contrast-limited adaptive histogram equalization (CLAHE) algorithm to enhance the contrast and clarity of the small object defects. Experimental results show that the proposed YOLOv11s-CD has superior performance compared with several other YOLO algorithms, whose mAP@0.5 has increased to 92.3% and AP of small object detection has significantly increased to 91.3%.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-10\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11145194/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11145194/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
YOLOv11s-CD: An Improved YOLOv11s Method for Catenary Dropper Fault Detection
The catenary dropper (CD) fault detection is an important technical means to ensure the train current collection quality and operational safety. The existing you only look once (YOLO) detection algorithms need improvement in terms of accuracy, especially in the detection of small objects. To address the problem, this article proposes a CD fault detection model based on improved YOLOv11s, named YOLOv11s-CD. First, a four-detection head structure DASFFHead is designed to achieve multiscale feature fusion by integrating a small object detection layer into the neck network and combining a dynamic adaptive spatial feature fusion (DASFF) module. Subsequently, the squeeze–excitation and attention module (SEAM) attention mechanism is embedded in the neck network layer to extract more small object features in occluded areas. In addition, combining the InnerIoU and CIoU methods, the InnerCIoU loss function is designed to enhance the small object detection ability. Finally, the effectiveness and accuracy of the proposed model are validated on the dataset, which is processed by the optimized contrast-limited adaptive histogram equalization (CLAHE) algorithm to enhance the contrast and clarity of the small object defects. Experimental results show that the proposed YOLOv11s-CD has superior performance compared with several other YOLO algorithms, whose mAP@0.5 has increased to 92.3% and AP of small object detection has significantly increased to 91.3%.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.