目前输送带跑偏检测研究主要集中于提取输送带边缘的直线特征，该方式需设定特定阈值，易受环境因素的制约，导致检测速度慢、精度不高。针对该问题，提出了一种基于改进RT−DETR的井下输送带跑偏故障检测算法，使用改进RT−DETR直接对一组托辊检测，根据左右托辊的暴露程度识别是否跑偏。针对实时检测转换器（RT−DETR）主干网络进行3个方面的改进：① 为了减少主干网络的参数量和浮点运算数量（FLOPs），使用FasterNet Block替换ResNet34中的BasicBlock；② 为了提升模型的精度和效率，在FasterNet Block结构中，引入结构重参数化的思想；③ 为了提升FasterNet Block在特征提取方面的性能，引入了高效多尺度注意力机制(EMA)，更加有效地捕捉全局和局部特征图。为了拓展感受野并捕获更有效、更广泛的上下文信息，以获得更为丰富的特征表达，采用改进高级筛选特征融合金字塔网络（HS−FPN）来优化多尺度特征融合。实验结果表明，与基准模型相比较，改进RT−DETR模型的参数量和FLOPs分别减少了8.4×10⁶ 个和17.8 G，mAP@0.5达94.5%，严重跑偏检测精度达99.2%，检测速度达41.0 帧/s，优于TOOD，ATSS等目标检测模型，满足煤矿生产对目标检测实时性和准确性的需求。

Current research on conveyor belt deviation detection mainly focuses on extracting the straight-line features of belt edges. The method requires setting specific thresholds and is easily affected by environmental factors, resulting in slow detection speed and low accuracy. To address the issue, an underground conveyor belt deviation fault detection algorithm based on an improved real-time detection transformer (RT-DETR) was proposed. The improved RT-DETR was used to directly detect a set of idlers and identify deviation based on the exposure degree of the left and right idlers. Three improvements were made to the RT-DETR backbone network: ① To reduce the number of parameters and floating-point operations (FLOPs), FasterNet Block was used to replace the BasicBlock in ResNet34. ② To enhance model accuracy and efficiency, the concept of structural reparameterization was introduced into the FasterNet Block structure. ③ To improve the feature extraction capability of FasterNet Block, an efficient multi-scale attention (EMA) Module was incorporated to capture both global and local feature maps more effectively. To expand the receptive field and capture more effective and comprehensive contextual information for richer feature representation, an improved high-level screening feature fusion pyramid network (HS-FPN) was adopted to optimize multi-scale feature fusion. Experimental results showed that compared to the baseline model, the improved RT-DETR reduced parameters and FLOPs by 8.4×10⁶ and 17.8 G, respectively. The mAP@0.5 reached 94.5%, with a severe deviation detection accuracy of 99.2% and a detection speed of 41.0 frame per second, outperforming TOOD and ATSS object detection models, meeting the real-time and accuracy requirements of coal mine production.