Work

Transmission Equipment Segmentation via Cross-Directional Convolution and Hierarchical Attention Mechanisms

Year: 2026

Type: article

Abstract: Precise segmentation of transmission equipment is crucial for ensuring secure power grid operation, yet practical deployment faces substantial challenges including the preservation of elongated morphological characteristics of transmission lines and accurate boundary localization for complex transmission tower structures. This paper proposes a novel segmentation method that synergistically integrates cross-directional convolutions with multi-layer attention mechanisms within the YOLO11 framework. The designed C3x cross-directional convolution module incorporates orthogonal convolutional operations during feature extraction, enabling independent enhancement of feature responses along horizontal and vertical dimensions. This architecture effectively captures continuous morphological characteristics of elongated targets while mitigating fragmentation artifacts. Additionally, the proposed Multi-Layer Cascaded Attention (MLCA) module employs a progressive fusion strategy combining spatial and channel attention, significantly augmenting the network’s capacity to extract multi-scale semantic information while maintaining computational efficiency. This design particularly enhances boundary detail preservation for structurally complex targets. Experimental evaluations on the TTPLA dataset (comprising 1232 images across 4 categories) demonstrate remarkable performance improvements: bounding box detection achieves 72.56% [email protected] and mask segmentation reaches 68.37% [email protected], representing gains of 2.97% and 4.52% respectively over the baseline YOLO11 model. The Mask F1 score improves from 67.85% to 71.76%, comprehensively validating the proposed method’s effectiveness in enhancing segmentation capabilities for both elongated and morphologically complex targets. These results substantiate the practical applicability of the proposed approach for intelligent transmission infrastructure monitoring systems.

Source: Electronics

DOI: 10.3390/electronics15081657

Authors: Congcong Yin, Ke Zhang, Yuqian Zhang, Zhongjie Zhu

Institutions: State Grid Corporation of China (China), State Grid Zhejiang Electric Power Company (China), Zhejiang Wanli University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896