An Optimized Algorithm for Transmission Line Anomaly Detection Based on Improved YOLOv11n.
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| Title: | An Optimized Algorithm for Transmission Line Anomaly Detection Based on Improved YOLOv11n. |
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| Authors: | Bai, Jingpan1,2,3,4 (AUTHOR), Shi, Yan1,2,3,4 (AUTHOR), Chen, Yuan1,2,3,4 (AUTHOR), Ji, Houling1,2,3,4 (AUTHOR) jhouling@yangtzeu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1873. 23p. |
| Subjects: | Object recognition (Computer vision), Feature extraction |
| Abstract: | Highlights: What are the main findings? A novel C3k2_DFF module is designed to enhance the model's capability for multi-scale object detection. The SEAM attention mechanism is introduced to improve detection performance in occlusion scenarios. What are the implications of the main findings? The proposed method effectively addresses feature extraction challenges for targets of varying sizes and overlapping objects. This study provides a robust solution for complex environment perception, significantly boosting the overall detection accuracy. To address the issues of low accuracy in transmission line anomaly detection and recognition caused by challenges such as multi-scale targets and partial occlusion, this paper proposes an optimized transmission line anomaly detection algorithm based on improved YOLOv11n. Firstly, an improved Cross Stage Partial with kernel size 2 (C3k2_DFF) module takes the place of the original C3k2 module in the backbone network. It adaptively fuses multi-scale local features and dynamically selects salient channel-wise and spatial features according to its global information during fusion to enhance the model's feature representation. Secondly, a Separated and Enhancement Attention Module (SEAM) attention mechanism is introduced to enhance the unoccluded area feature response to compensate for the occluded area response deficit, suppressing the background features that interfere with the model and improving the model's occluded target perception capability. Experimental results on our self-constructed dataset indicate that the proposed improved YOLOv11n model achieves precision, recall, mAP50, and mAP50-95 of 94.2%, 90.8%, 94.3%, and 68.0%, respectively. Compared with the baseline model, it represents improvements of 2.0%, 1.3%, 1.6%, and 2.9%, while the parameters and GFLOPs increase by only 6.6% and 4.8%, demonstrating superior detection performance. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel C3k2_DFF module is designed to enhance the model's capability for multi-scale object detection. The SEAM attention mechanism is introduced to improve detection performance in occlusion scenarios. What are the implications of the main findings? The proposed method effectively addresses feature extraction challenges for targets of varying sizes and overlapping objects. This study provides a robust solution for complex environment perception, significantly boosting the overall detection accuracy. To address the issues of low accuracy in transmission line anomaly detection and recognition caused by challenges such as multi-scale targets and partial occlusion, this paper proposes an optimized transmission line anomaly detection algorithm based on improved YOLOv11n. Firstly, an improved Cross Stage Partial with kernel size 2 (C3k2_DFF) module takes the place of the original C3k2 module in the backbone network. It adaptively fuses multi-scale local features and dynamically selects salient channel-wise and spatial features according to its global information during fusion to enhance the model's feature representation. Secondly, a Separated and Enhancement Attention Module (SEAM) attention mechanism is introduced to enhance the unoccluded area feature response to compensate for the occluded area response deficit, suppressing the background features that interfere with the model and improving the model's occluded target perception capability. Experimental results on our self-constructed dataset indicate that the proposed improved YOLOv11n model achieves precision, recall, mAP50, and mAP50-95 of 94.2%, 90.8%, 94.3%, and 68.0%, respectively. Compared with the baseline model, it represents improvements of 2.0%, 1.3%, 1.6%, and 2.9%, while the parameters and GFLOPs increase by only 6.6% and 4.8%, demonstrating superior detection performance. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121873 |
