Aug 2026· International Conference on Computer Vision and Pattern Analysis· Vol 14296, pp. 1429611 - 1429611-7· 0 citations· 17 references
Engineering
TL;DR
A lightweight YOLOv11-based foreign object detector is designed, using StarNet to reconstruct the backbone, reducing redundant parameters and computational cost, and SDIoU is introduced for bounding box regression, which improves the localization of multi-scale targets.
Abstract
In power inspection scenarios, foreign objects are usually difficult to detect because of their small size, complex surrounding backgrounds, and limited computational resources for deployment. To improve detection efficiency under these conditions, this study designs a lightweight YOLOv11-based foreign object detector. StarNet is used to reconstruct the backbone, reducing redundant parameters and computational cost. The C3k2-DSAM module is added to the neck to reinforce fine-grained object features and weaken background interference. In addition, SDIoU is introduced for bounding box regression, which improves the localization of multi-scale targets. Experiments on the self-built dataset show that the improved model obtains 86.5% mAP@0.5, 62.1% mAP@0.5:0.95, and 89.3% Precision at 19.2 GFLOPs, demonstrating competitive accuracy with low computational cost.
The YOLOv12 network is adopted as the baseline model and the ADown module is introduced to improve downsampling efficiency while maintaining lightweight performance, and the BN-CGLU is incorporated into the A2C2f module to enhance the model’s nonlinear representation capability.
Ao-Bo Yue, Puchun Chen, Yan Yang· Journal of Real-Time Image P...· 0 citations
Insulators play a vital role in ensuring the safe and stable operation of transmission lines. This study develops IDD-YOLO, an engineering-oriented lightweight detector for UAV-based insulator inspection, with emphasis on reducing model complexity while preserving the weak visual information of localized defects. Ghost...
The DCNv3 (Deformable Convolution v3) is embedded into the backbone network to replace the traditional convolutional layers, enhancing the ability to extract features of irregular defects and retains the lightweight advantage and can meet the requirements of on-site real-time inspection.
Yu-Jie Sheng, Sang Junjie, Lu Li· Digital Signal and Computer...· 0 citations
This paper proposes an improved lightweight YOLO11s algorithm to address the issues of excessive model complexity and poor performance when detecting small objects in road defect detection. By incorporating the StarNet architecture to reconstruct the backbone network, the method utilizes star-shaped operations to enhan...
Rui Zhang, Yonghao Dong· Digital Signal and Computer...· 0 citations
An efficient and practical solution for real-time foreign object detection under challenging railway conditions by integrating an adaptive brightness enhancement network (ABEN) and a lightweight train foreign object detection network (LTFD-Net).
Hui Lin, Junqi Li, Baolin Liu et al.· Railway Engineering Science· 0 citations
Transmission lines are typically exposed to open environments for extended periods, rendering them susceptible to foreign object intrusions that can lead to power system failures. Conventional detection methods rarely account for complex weather conditions. To address the issue of low detection accuracy under such scen...
Xudong Luo· International Conference on...· 0 citations
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