LMFC-DETR: A Lightweight Model for Real-Time Detection of Suspended Foreign Objects on Power Lines

Meters in electrical equipment are often potentially threatened by foreign objects suspended on power lines, which can raise the risk of short circuits or ground faults in the line, leading to problems such as loss of power supply to the meter and inaccurate readings. To address this problem, this article proposes a lightweight fast real-time detection network architecture, the lightweight multiscale faster capture detection transformer (LMFC-DETR) for detecting the presence of foreign objects in power lines, which fuses multilevel feature maps through the repeat parameter acceleration block (RPAB) and the faster gated convolution linear units (FGCLUs) modules in the backbone network, effectively reducing the computational burden and reducing the common degradation risk of deep networks. In addition, introducing the location feature interactive attention (LFIA) mechanism and multiscale integration of lightweight features (MIOLF) structure into efficient hybrid encoders, significantly enhanced the fusion and expression capabilities of Fusion-X features. Aiming at the challenges brought by low quality images and small target size datasets, this article designs Fusion-E module in MIOLF structure to retain and strengthen key information, and the global dynamic sequence fusion block (GDSFB) to facilitate deep interaction and complementarity among feature maps, thereby greatly improve the detection performance of the model. Experimental results show that LMFC-DETR achieves detection accuracies of 87.9 and 89.5 in the public and self-made foreign object detection HD dataset, respectively, with an average the frames per second (FPS) of 50, fully demonstrating its excellent performance and great potential in real-time foreign object detection tasks on power lines. Our code and the blocks we made are shown in https://github.com/Tianyu-Li-S/LMFC-DETR/tree/main/LMFC_Block