# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 6 Conclusions  
This paper proposes a foreign-object-detection model for transmission lines based on improved YOLOv8m. Our model autonomously detects anomalies in aerial images of power transmission lines, reducing the workload associated with anomaly detection and enhancing inspection efficiency.  
Addressing the challenges of foreign-object detection along power transmission lines, we use three key improvements: firstly, to mitigate issues related to occlusion, a Global Attention Mechanism (GAM) was introduced, enhancing focus on obscured targets. Secondly, in response to the diversity of foreign-object categories and complex backgrounds, the SPPCSPC module was integrated, boosting the model's multi-scale feature-extraction capabilities. Thirdly, the Focal-EIoU loss function was introduced to address imbalances between high- and low-quality samples, expediting model convergence and augmenting detection accuracy. The refined YOLOv8m model exhibited significant enhancements, elevating mAP_0.5 from 92.8% to 95.5%, mAP_0.5:0.95 from 76.4% to 80.4%, and recall from 85.9% to 91.9%.  
No publicly available datasets for power transmission line foreign-object detection have been provided in previously published literature, thus hindering experimentation with our enhanced model on alternative datasets. The dataset utilized in this study originates from the Electric Power Research Institute, Yunnan Power Grid Co., Ltd., featuring geolocation data in certain images. Upon removing location information and obtaining approval from the aforementioned organization, we plan to release this dataset publicly, facilitating future research in related domains.  
Although the upgraded model demonstrates the potential to be applied in practical detection scenarios for foreign objects on power transmission lines, its detection speed is presently insufficient for direct application on terminal CPU devices. Subsequent work will focus on adopting lighter models, such as YOLOv8n, to enhance model detection speed while maintaining detection accuracy.  
Conceptualization, Z.W.; methodology, Z.W.; software, Z.W.; validation, Z.W.; formal analysis, Z.W., G.Y., H.Z., Y.M. ([PERSON]) and Y.M. ([PERSON]); investigation, Z.W., G.Y., H.Z., [PERSON] ([PERSON]) and Y.M. ([PERSON]); resources, Z.W. and G.Y.; data curation, Z.W.; writing--original draft preparation, Z.W. and G.Y.; writing--review and editing, Z.W. and G.Y.; funding acquisition, G.Y., H.Z., [PERSON] ([PERSON]) and [PERSON] ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Projects of Yunnan Province (Grant No. 202202 AD080004), the Natural Science Foundation of China (Grant No. 62061049, 12263008), the Yunnan Provincial Department of Science and Technology-Yunnan University Joint Special Project for Double-Class Construction (Grant No. 202201 BF070001-005), and the Application and Foundation Project of the Yunnan Province (Grant No. 202001 BB050032).  
The data presented in this study are available on request from the corresponding author. The data is not publicly available due to the following reason. It is provided by Yunnan Power Grid Co., Ltd. and can only be released after obtaining approval from Yunnan Power Grid Co., Ltd.  
**Acknowledgments:** Thank you to Yunnan Power Grid Co., Ltd. for providing the foreign object dataset.  
**Conflicts of Interest:** Author [PERSON], [PERSON] and [PERSON] are employed by the Yunnan Power Grid Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.