TL-DETR: Efficient transmission line defect detection for edge deployment

Abstract

Visual inspection is critical for power system maintenance, yet deploying high-performance detection models on resource-constrained edge devices remains challenging due to complex background interference, extreme defect scale variations, and high computational overhead. This paper presents TL-DETR, a specialized detection framework that integrates multi-scale feature enhancement and dynamic sparse attention to achieve accurate and efficient transmission line defect detection for edge deployment. First, a ResNet-50-TL backbone network incorporating a multi-scale feature enhancement module is designed to preserve fine-grained features. Subsequently, the neck network integrates Attention-based Intra-scale Bi-level Routing and a channel shuffle mechanism to precisely focus on critical defects and reduce parameter count. Furthermore, a multi-scale attention mechanism is introduced to accomplish pixel-level recalibration through cross-spatial learning. Experiments on the CableInspect-ADs dataset demonstrate that the precision and mAP50 of TL-DETR reach 91.4% and 86.0%, respectively, representing improvements of 3.2% and 2.9% over the baseline RT-DETR. These results indicate that the model effectively balances accuracy and computational efficiency, demonstrating theoretical viability for practical edge deployment. Generalization experiments confirm that the model exhibits excellent generalization capabilities for detecting insulators, vibration dampers, and bolts, aligning closely with the engineering requirements for precise perception of minute defects.