SSFF-DETR: A Surface Contaminant Detection Transformer for Microsystem Devices with Scale Sequence Feature Fusion

Microsystem devices are widely used in key fields such as aerospace. The various contaminants generated during their manufacturing process have the characteristics of diverse forms and are easily affected by background interference, making them difficult to detect. To solve this problem, this paper proposes a surface contaminant detection transformer for microsystem devices with scale sequence feature fusion (SSFF-DETR). This model is based on the real-time detection transformer (RT-DETR) framework. The faster efficient channel attention (Faster-ECA) was constructed as the backbone network, enhancing the extraction ability and computational efficiency of key features of contaminants. By introducing the dynamic feature region collaborative attention (DFRCA) at the end of the backbone network, the contrast between contaminant features and the background was effectively enhanced, thereby improving the model’s ability to identify contaminants. An Encoder based on scale sequence feature (SSF) and triple-branch feature fusion (TFF) is designed. By enhancing multi-scale representation, it effectively retains the detailed features of contaminants in complex backgrounds and alleviates the problem of feature loss during transmission in deep networks. The experimental results show that compared with the RT-DETR model, the SFFE-DETR model has achieved an increase of 2.6% in mean average precision (mAP). At the same time, the Giga Floating-Point Operations Per Second (GFLOPs) have decreased by 2G, and the params have reduced by 0.8 M. This provides a feasible solution for the high-precision and high-efficiency automated detection of surface contaminants in microsystem devices.