{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T16:33:14Z","timestamp":1789749194004,"version":"4.0.1"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technology projects of China Southern Power Grid Co., Ltd.","award":["CGYKJXM20230142"],"award-info":[{"award-number":["CGYKJXM20230142"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper proposes a lightweight DGNCA-Net insulator defect detection algorithm based on improvements to the YOLOv11 framework, addressing the issues of high computational complexity and low detection accuracy for small targets in machine vision-based insulator defect detection methods. Firstly, to enhance the model\u2019s ability to perceive multi-scale targets while reducing computational overhead, a lightweight Ghost-backbone network is designed. This network integrates the improved Ghost modules with the original YOLOv11 backbone layers to improve feature extraction efficiency. Meanwhile, the original C2PSA module is replaced with a CSPCA module incorporating Coordinate Attention, thereby strengthening the model\u2019s spatial awareness and target localization capabilities. Secondly, to improve the detection accuracy of small insulator defects in complex scenes and reduce redundant feature information, a DC-PUFPN neck network is constructed. This network combines deformable convolutions with a progressive upsampling feature pyramid structure to optimize the Neck part of YOLOv11, enabling efficient feature fusion and information transfer, while retaining the original C3K2 module. Additionally, a composite loss function combining Wise-IoUv3 and Focal Loss is adopted to further accelerate model convergence and improve detection accuracy. Finally, the effectiveness and advancement of the proposed DGNCA-Net algorithm in insulator defect detection tasks are comprehensively validated through ablation studies, comparative experiments, and visualization results.<\/jats:p>","DOI":"10.3390\/a18080528","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T15:53:37Z","timestamp":1755705217000},"page":"528","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["DGNCA-Net: A Lightweight and Efficient Insulator Defect Detection Model"],"prefix":"10.3390","volume":"18","author":[{"given":"Qiang","family":"Chen","sequence":"first","affiliation":[{"name":"Baise Bureau CSG EHV Power Transmission Company, Baise 533000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanfeng","family":"Luo","sequence":"additional","affiliation":[{"name":"Baise Bureau CSG EHV Power Transmission Company, Baise 533000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wu","family":"Yuan","sequence":"additional","affiliation":[{"name":"Baise Bureau CSG EHV Power Transmission Company, Baise 533000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guiyang Bureau CSG EHV Power Transmission Company, Guiyang 550081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8729-2880","authenticated-orcid":false,"given":"Yunshou","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou 516007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Q., Hu, Z., Li, E., Wu, G., Yang, W., Hu, Y., Peng, W., and Sun, J. (2025). YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment. Energies, 18.","DOI":"10.3390\/en18071668"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ji, Y., Zhang, D., He, Y., Zhao, J., Duan, X., and Zhang, T. (2025). Improved YOLO11 Algorithm for Insulator Defect Detection in Power Distribution Lines. Electronics, 14.","DOI":"10.3390\/electronics14061201"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, J., Miao, S., Kang, R., Cao, L., Zhang, L., and Ren, Y. (2025). Insulator Defect Detection Algorithm Based on Improved YOLOv11n. Sensors, 25.","DOI":"10.3390\/s25051327"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1109\/TPWRD.2021.3116600","article-title":"Insulator Breakage Detection Utilizing a Convolutional Neural Network Ensemble Implemented With Small Sample Data Augmentation and Transfer Learning","volume":"37","author":"She","year":"2022","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1109\/TSMC.2018.2871750","article-title":"Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks","volume":"50","author":"Tao","year":"2020","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26245","DOI":"10.1109\/JSEN.2023.3315280","article-title":"Automatic Overheating Defect Diagnosis Based on Rotated Detector for Insulator in Infrared Image","volume":"23","author":"Lin","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1729881418773943","DOI":"10.1177\/1729881418773943","article-title":"Computer vision\u2013based automatic rod-insulator defect detection in high-speed railway catenary system","volume":"15","author":"Han","year":"2018","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3127641","article-title":"DefGAN: Defect Detection GANs With Latent Space Pitting for High-Speed Railway Insulator","volume":"70","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4182652","DOI":"10.1049\/2024\/4182652","article-title":"Insulator Defect Recognition Based on Vision Big-Model Transfer Learning and Stochastic Configuration Network","volume":"2024","author":"Liu","year":"2024","journal-title":"IET Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e34298","DOI":"10.1002\/mop.34298","article-title":"Detection of defects in composite insulators based on laser induced plasma combined with machine learning","volume":"66","author":"Xu","year":"2024","journal-title":"Microw. Opt. Technol. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3493878","article-title":"LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection","volume":"73","author":"Li","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Deng, C., Sun, Q., Wu, Z., Zou, L., Zhang, G., and Li, W. (2024). Lightweight Detection Methods for Insulator Self-Explosion Defects. Sensors, 24.","DOI":"10.3390\/s24010290"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Zhu, X., Liao, C., Shi, D., and Qu, W. (2022). Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model. Appl. Sci., 12.","DOI":"10.3390\/app12031207"},{"key":"ref_14","first-page":"1","article-title":"CACS-YOLO: A Lightweight Model for Insulator Defect Detection Based on Improved YOLOv8m","volume":"73","author":"Cao","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lu, Y., Li, D., Li, D., Li, X., Gao, Q., and Yu, X. (2024). A Lightweight Insulator Defect Detection Model Based on Drone Images. Drones, 8.","DOI":"10.3390\/drones8090431"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1007\/s11554-024-01557-y","article-title":"RDB-YOLOv8n: Insulator defect detection based on improved lightweight YOLOv8n model","volume":"21","author":"Jiang","year":"2024","journal-title":"J. Real-Time Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, C., Yi, W., Liu, M., Wang, Y., Hu, S., and Wu, M. (2023). A Lightweight Network Based on Improved YOLOv5s for Insulator Defect Detection. Electronics, 12.","DOI":"10.3390\/electronics12204292"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Han, W., Cai, Z., Li, X., Ding, A., Zou, Y., and Wang, T. (2025). LMD-YOLO: A lightweight algorithm for multi-defect detection of power distribution network insulators based on an improved YOLOv8. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0314225"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110464","DOI":"10.1016\/j.epsr.2024.110464","article-title":"Insulator defect detection in transmission line based on an improved lightweight YOLOv5s algorithm","volume":"233","author":"Wei","year":"2024","journal-title":"Electr. Power Syst. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s11554-024-01589-4","article-title":"Insulator defect detection algorithm based on adaptive feature fusion and lightweight YOLOv5s","volume":"22","author":"He","year":"2025","journal-title":"J. Real-Time Image Process."},{"key":"ref_21","first-page":"1","article-title":"ID-YOLO: A Multimodule Optimized Algorithm for Insulator Defect Detection in Power Transmission Lines","volume":"74","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gong, C., Jiang, W., Zou, D., Weng, W., and Li, H. (2024). An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8. Appl. Sci., 14.","DOI":"10.3390\/app14198770"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1109\/TPWRD.2024.3467915","article-title":"LA-YOLO: Bidirectional Adaptive Feature Fusion Approach for Small Object Detection of Insulator Self-Explosion Defects","volume":"39","author":"Liu","year":"2024","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106781","DOI":"10.1109\/ACCESS.2024.3436919","article-title":"An Insulator Location and Defect Detection Method Based on Improved YOLOv8","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, Z.Y., and Tang, X. (2025). Transmission line insulator defect detection algorithm based on MAP-YOLOv8. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-92445-3"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., and Cho, K. (2019). Augmentation for small object detection. arXiv.","DOI":"10.5121\/csit.2019.91713"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Z., Su, Y., Kang, F., Wang, L., Lin, Y., Wu, Q., Li, H., and Cai, Z. (2025). PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection. Sensors, 25.","DOI":"10.3390\/s25020348"},{"key":"ref_28","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Han, J., and Ding, G. (2022, January 18\u201324). Scaling Up Your Kernels to 31 \u00d7 31: Revisiting Large Kernel Design in CNNs. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13\u201319). GhostNet: More Features From Cheap Operations. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_31","unstructured":"Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105004","DOI":"10.1109\/ACCESS.2024.3434687","article-title":"YOLO-PowerLite: A Lightweight YOLO Model for Transmission Line Abnormal Target Detection","volume":"12","author":"Liu","year":"2024","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"79451","DOI":"10.1109\/ACCESS.2025.3566907","article-title":"YOLO-HF: Early Detection of Home Fires Using YOLO","volume":"13","author":"Peng","year":"2025","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Varghese, R., and Sambath, M. (2024, January 18\u201319). YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India.","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/528\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:31:31Z","timestamp":1760034691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/528"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":35,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["a18080528"],"URL":"https:\/\/doi.org\/10.3390\/a18080528","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]}}}