Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR

Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. To address this trade-off, this study introduces an improved detection method based on the Real-Time DEtection TRansformer(RT-DETR) framework. First, a large-kernel attention mechanism is integrated into the backbone to enhance multi-scale feature extraction and fusion, while reducing architectural redundancy. Second, the RepC3 structure within the cross-scale fusion module is replaced with a module based on the generalized-efficient layer aggregation network that uses a more efficient layer aggregation strategy to improve feature localization. Finally, the Adown downsampling module is introduced, employing a multi-path design that reduces parameter count while preserving critical feature details during scale reduction. Experimental results on our industrial filter surface defect dataset show that the enhanced RT-DETR model achieves a mean average precision of 97.6%, a 7.3 percentage point increase over the baseline. Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. Generalization experiments on the public NEU-DET dataset and GC10-DET dataset further confirm the model's robustness and effectiveness, demonstrating its suitability for industrial applications requiring both high accuracy and lightweight deployment.