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An improved defect recognition framework for casting based on DETR algorithm
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AI
DOI：10.1007/s42243-023-00920-w.png)
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Abstract
En 中文
The current casting surface defect detection algorithms suffer from poor small target defect recognition and imbalance between detection performance and detection time. An improved algorithmic framework for casting defect detection was proposed based on the DEtection TRansformer (DETR) algorithm. The algorithm takes ResNet with an efficient channel attention (ECA)-Net module as the backbone network. In addition, based on the original algorithm architecture, dynamic anchor boxes, improved multi-scale deformable attention module, and SIoU loss function are introduced to improve the sensitivity of transformer structure to input location information and scale size, and the small target defect detection performance is effectively improved. The recognition performance of the algorithm in a self-built casting defect dataset was studied. The improved DETR algorithm has 97.561% accuracy in recognizing two defects, namely sandinclusion and notch, with the detection rate being improved by 65.854% and 17.073% compared with the original DETR and you only look once (Yolo)-V5, respectively. This algorithm verifies the applicability of the transformer architecture target detection algorithm for casting defect detection tasks and provides new ideas for detecting other similar application scenarios.
Keywords:
Casting defect recognition
DEtection TRansformer
Small target detection
Deep learning
Attention mechanism
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