To address the issues such as low precision in small object recognition and large-scale variations in the detection of train bearing roller defects， a detection algorithm based on FHC-DETR（Fourier-fused High-low frequency interactive and Context-aware DEtection TRansformer） was proposed. Firstly， aiming at the problems of complex model computation and small object features being disturbed by noise， a frequency-domain feature extraction module C2f-FG （C2f with Fourier-Gated bottleneck） was designed for feature extraction. It synchronously acquired spatial-domain local features and frequency-domain global features via Fourier transform， and their fusion enhanced the accuracy of small-object detection while reducing computational complexity. Secondly， to tackle feature confusion caused by variations in defect scale， a high-low-frequency feature interaction module， HiLo （High-Low frequency）， was introduced. The high-frequency branch focused on local defect textures， while the low-frequency branch captured overall semantics via global attention， thereby improving multi-scale adaptability. Subsequently， to resolve the issue of small object feature attenuation in feature fusion， a Context-aware Multi-scale Bidirectional Feature Pyramid Network （CM-BiFPN） was constructed. By dynamically perceiving context and strengthening cross-layer interaction， it reduced feature transmission loss and improved fusion efficiency. Finally， the EMASlideVarifocalLoss adaptive loss function was adopted to dynamically adjust classification thresholds and optimize weights of hard examples， further enhancing localization and category discrimination capabilities. Experimental results show that FHC-DETR achieves a mean Average Precision （mAP） of 91.5%， which is 2.3 percentage points higher than that of the original RT-DETR （Real-Time DETR）. Additionally， its parameter count is reduced by 28.1%， its computational load is reduced by 23.0%， and its memory usage is reduced by 23.7%， demonstrating a balance between precision and efficiency and confirming its practicality in industrial scenarios.