Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5

Abstract

The images captured using UAVs during inspection often contain a great deal of small targets related to transmission lines. These vulnerable elements are critical for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential target aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, to tackle the challenges posed by the issues of complex background and environmental interference impacting small targets, an ECA attention module is integrated into the network. The module effectively enhances the network’s focus on small targets, while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, making the network more sensitive to small targets. Lastly, the network utilizes the EIOU_Loss as the regression loss function to improve the positioning accuracy of small targets, especially when they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P (precision) of 96.0% and an average accuracy (mAP) of 97.0% for small-target detection in transmission lines.

Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

“…In research on transmission line components and environment, Chen et al [42] improved a YOLOv5s model for key-component detection of power transmission lines. Cheng et al [43] proposed a detection algorithm called P2E-YOLOv5 to detect a small target. Chen et al [44] proposed an optimized YOLOv7-tiny model for smoke detection in power transmission lines.…”