关键词: 钛合金连接件, 表面缺陷检测, DEIM, 多尺度特征, 交叉注意力

Abstract: Abstract: The surface defect detection of titanium alloy connectors is very important for the safety and reliability of aviation equipment. However, the existing deep learning models have problems such as difficulty in taking into account multi-scale defects, confusion between defects and background, and easy loss of fine-grained features in the face of connectors with complex morphology. In order to solve the above problems, this paper proposes a new model architecture DEIM-MFE based on the DEIM-RTdetr model. Firstly, the real images of titanium alloy connectors in the actual industrial scene are collected, and a special data set (TAC-Dataset) for surface defects of titanium alloy connectors for aero-engines is constructed. Secondly, a new encoder structure, multi-scale feature enhanced encoder (MFE Encoder), is proposed. The multi-scale feature alignment and cross-attention fusion module (MFAC) enhances the fusion ability of multi-scale semantic information by performing spatial alignment and cross-attention interaction on different levels of features. The local residual enhancement module (LRE) uses convolution operation to strengthen local texture and edge information, and improves the model 's ability to distinguish between defect areas and complex backgrounds. The shallow feature recovery module (SFR) is guided by the output multi-scale enhanced semantic features, and deeply reconstructs the shallow features through a multi-branch aggregation structure, so that the details and semantics fully interact, thereby enhancing the ability to characterize small defects. The experimental results on TAC-Dataset show that compared with the benchmark model, the proposed model improves Precision, Recall, F1-Score and mAP @ 0.5 by 2.37, 1.73, 2.10 and 2.02 percentage points respectively. This model shows good recognition and positioning ability in the surface defect detection task of complex connectors, and provides a reliable and effective technical solution for the surface quality detection of key connectors.

Key words: titanium alloy connectors, surface defects detection, DEIM, multi-scale features, Cross-Attention