Work

End to end real time deep learning framework for synergistic flaw detection in complex X ray images

Year: 2026

Type: article

Abstract: The safe operation of the transmission line is significantly influenced by the structural integrity of the tension clamp, a critical component of the power system. This investigation introduces a real-time approach to defect detection and merging in X-ray images that employs Swin Transformer and EfficientNet. The objective is to improve the intelligence and efficacy of tension clamp detection. The system is composed of two primary modules: The encoder-decoder architecture of E-RSNet is constructed using EfficientNet, which incorporates a multi-head self-attention mechanism and recursive feature fusion to achieve high-precision image stitching. The Swin Transformer is the foundation of E-DDT, which incorporates a multi-scale attention mechanism and an adaptive feature pyramid network to improve the recognition capabilities of a variety of minor defects. Attention-guided feature fusion and multi-task learning frameworks enable the system to optimize image stitching and defect detection tasks in a coordinated manner. Lightweight methodologies, such as dynamic pruning and mixed precision quantization, are implemented to satisfy the real-time requirements of on-site detection. A prototype system was constructed and experimentally validated to confirm the end-to-end feasibility and real-time performance of the proposed framework. The proposed method outperforms existing techniques in image stitching quality, defect detection accuracy, and computational efficiency, as demonstrated by empirical data validation and simulation experiments. This approach is a viable option for the intelligent and portable detection of electrical equipment.

Source: Discover Artificial Intelligence

DOI: 10.1007/s44163-026-01462-2

Authors: Mingyang You, Mindi Geng, Wenzhi Zeng, Yuanhang Wang, Jiarui Huang, Jiang Lin

Institution: Guizhou Power Grid Co., Ltd. (China)

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,590