A lightweight cross-scale EDS-DETR model for hazard detection in transmission corridors

Visual inspection technology has been widely employed for identifying external hazards in transmission corridors. However, Convolutional Neural Networks (CNNs) exhibit limitations in multi-scale target detection within complex environments while struggling to balance accuracy and lightweight design. To address these issues, we propose a lightweight cross-scale detection model, called EDS-DETR. First, we improve the ResNet18 backbone by integrating the efficient multi-scale attention module with partial convolutions to improve the computational efficiency and representational capability. Second, DySample is introduced into the encoder to efficiently restore feature resolution of inspection images with minimal computational cost, meanwhile preserving feature details and enhancing dynamic perception capability. Finally, we adopt the shapeIoU loss function to enhance the detection accuracy of external hazards and accelerate model convergence. Experiments on a self-built dataset show that the proposed EDS-DETR model achieves precision, recall, and mAP@0.5 scores of 91.4%, 85.1%, and 93.1%, respectively. Notably, our method exhibits significant advantages in model efficiency, requiring 13.4% fewer parameters and 14.8% reduced model size compared to baseline approaches. Furthermore, EDS-DETR achieves an inference speed of 190 FPS, which satisfies the real-time requirement. The experimental results prove the effectiveness and practicability of the EDS-DETR model, contributing to the enhancement of reliability and safety in power transmission.