Cheng, Q.; Yuan, G.; Chen, D.; Xu, B.; Chen, E.; Zhou, H. Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5. Appl. Sci.2023, 13, 9386.
Cheng, Q.; Yuan, G.; Chen, D.; Xu, B.; Chen, E.; Zhou, H. Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5. Appl. Sci. 2023, 13, 9386.
Cheng, Q.; Yuan, G.; Chen, D.; Xu, B.; Chen, E.; Zhou, H. Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5. Appl. Sci.2023, 13, 9386.
Cheng, Q.; Yuan, G.; Chen, D.; Xu, B.; Chen, E.; Zhou, H. Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5. Appl. Sci. 2023, 13, 9386.
Abstract
The images captured by UAVs during inspection often contain numerous small targets related to transmission lines, which are critical and vulnerable elements 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 line 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, in order to address the challenges posed by the complex background and environmental interference that impact small targets, an ECA attention module is integrated into the network. This 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, which is 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, as they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P of 96.0% and an average accuracy (mAP) of 97.0% for small target detection in transmission lines.
Keywords
power line inspection; object detection; small targets; attention mechanisms; Loss function
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
