Article: Insulator defect detection based on improved YOLOX Journal: International Journal of Wireless and Mobile Computing (IJWMC) 2025 Vol.28 No.4 pp.427 - 437 Abstract: Detecting insulator defects is a crucial stage in the process of inspecting a power grid. To increase the accuracy of insulator defect identification, this study proposes a detection technique based on an enhanced version of the YOLOX algorithm, which can improve the accuracy of insulator defect detection while maintaining the detection speed. The algorithm adds the Efficient Channel Attention (ECA) mechanism to the Spatial Pyramid Pooling (SPP) based on YOLOX-s, i.e., ECA-SPP, adds the ECA mechanism to the feature pyramid for cross-channel interaction, suppresses interference information and strengthens the model's focus on target features according to the generated corresponding channel weight values, and then fuses the recalibrated feature maps with deeper features to improve the target feature expressiveness. The Complete Intersection over Union (CIoU) is utilised to calculate the loss, while the distance between the centroids of the two frames and the aspect ratio is factored into the penalty term, and the loss function is continuously changed and updated to accelerate the convergence speed of the model. The test findings demonstrate that the revised mean of Average Precision (mAP) algorithm for YOLOX achieves 93.21% accuracy, which is 6.17% higher than the original algorithm. Inderscience Publishers - linking academia, business and industry through research

Title: Insulator defect detection based on improved YOLOX

Authors: Jinbao Meng; Yan Wang; Zhu Xu; Juanyan Fang

Addresses: Department of Mathematics and Computer Science, Tongling University, Tongling, Anhui, China ' Department of Design Management, Woosong University, Daejeon, South Korea ' Department of Mathematics and Computer Science, Tongling University, Tongling, Anhui, China ' Department of Mathematics and Computer Science, Tongling University, Tongling, Anhui, China

Abstract: Detecting insulator defects is a crucial stage in the process of inspecting a power grid. To increase the accuracy of insulator defect identification, this study proposes a detection technique based on an enhanced version of the YOLOX algorithm, which can improve the accuracy of insulator defect detection while maintaining the detection speed. The algorithm adds the Efficient Channel Attention (ECA) mechanism to the Spatial Pyramid Pooling (SPP) based on YOLOX-s, i.e., ECA-SPP, adds the ECA mechanism to the feature pyramid for cross-channel interaction, suppresses interference information and strengthens the model's focus on target features according to the generated corresponding channel weight values, and then fuses the recalibrated feature maps with deeper features to improve the target feature expressiveness. The Complete Intersection over Union (CIoU) is utilised to calculate the loss, while the distance between the centroids of the two frames and the aspect ratio is factored into the penalty term, and the loss function is continuously changed and updated to accelerate the convergence speed of the model. The test findings demonstrate that the revised mean of Average Precision (mAP) algorithm for YOLOX achieves 93.21% accuracy, which is 6.17% higher than the original algorithm.

Keywords: insulator defect detection; YOLOX; efficient channel attention; CIoU; ECA-SPP.

DOI: 10.1504/IJWMC.2025.146643

International Journal of Wireless and Mobile Computing, 2025 Vol.28 No.4, pp.427 - 437

Received: 27 Jan 2023
Accepted: 02 Sep 2023
Published online: 10 Jun 2025 *

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Title: Insulator defect detection based on improved YOLOX

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