Real-time defect detection method for metal deposition based on artificial intelligence

A real-time defect detection method for metal deposition based on artificial intelligence, comprising:

• S1. acquiring a deposition defect dataset, and extracting key deposition features by using a backbone network;
• S2. constructing a high-efficiency hybrid encoder by combining an attention mechanism and multi-layer convolution operation, capturing high-level semantic features in the key deposition features and performing multi-scale feature fusion through the high-efficiency hybrid encoder;
• S3. adopting an Intersection over Union (IoU) aware query selection method to assign corresponding classification scores to key deposition features with different IoU values during a training process to constrain model learning, and selecting more accurate key deposition features in a decoding stage by optimizing object queries;
• and
• S4. mapping optimized objects to classification confidence and bounding boxes by using a decoder with an auxiliary prediction head, and accelerating training convergence through a denoising module in the decoder.