# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 5 Conclusions  
In response to the challenging task of accurately and efficiently detecting small targets in transmission lines, this paper proposes a novel detection method called P2E-YOLOv5. This method incorporates the ECA attention mechanism into the network, introduces high-resolution detection heads that exhibit higher sensitivity to small targets, and optimizes  
Figure 14: This figure presents a detailed comparison of the detection performance of various algorithms, including Faster-RCNN, RetinaNet, SSD, YOLOv3_spp, YOLOX, YOLOv5, and P2E-YOLOv5, on small targets in transmission line images captured in different scenes. the regression loss function using EIOU_Loss. These improvements significantly enhance the detection efficiency and capability of small targets amidst complex backgrounds. We evaluate the performance of our algorithm on a data set specially designed for small-target detection in power lines, and our findings demonstrate that the proposed method outperforms other detection algorithms, showcasing superior detection performance. Despite a slight increase in memory usage due to the addition of high-resolution detection heads, the method maintains a high level of detection accuracy. Considering the trade-off between detection accuracy and speed, our P2E-YOLOv5 detection method proves to be the optimal choice. For future work, our goal is to further reduce the computational costs associated with P2 detection heads while maintaining comparable detection accuracy. Additionally, we aim to continue improving the network's detection speed to enhance overall performance. Conceptualization, H.Z., G.Y. and Q.C. ; methodology, Q.C. and E.C. ; software, Q.C. ; validation, B.X. and D.C.; investigation, Q.C., D.C. and B.X. ; resources, D.C.; writing--original draft preparation, Q.C. ; writing--review and editing, H.Z. and Q.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Major Science and Technology Project in Yunnan Province, grant number 202202 AD080004. The APC was funded by Yunnan Province Science and Technology Department. Not applicable. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Transmission Line Small Target Defect Detection Method Based on Cascade R-CNN Algorithm. _Power Grids Clean Energy_**2022**, _38_, 19-27+36. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June 2016; pp. 770-778. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] A Transmission Line Target Detection Method with Improved SSD Algorithm. _Electrician_**2021**, _282_, 51-55. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] An improved YOLO network for small target foreign body detection in transmission lines. _J. N. Inst. Technol._**2022**, _20_, 7-14. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Perceptual generative adversarial networks for small object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26 July 2017; pp. 1222-1230. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Multi-scale target detection method for transmission lines based on feature pyramid algorithm. _Instr. User_**2019**, _26_, 15-18. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Transmission line power devices and abnormal target detection based on improved CenterNet. _High-Volt Technol._**2023**, 1-11. [CrossRef]
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