Algorithm optimization of chip surface defect detection based on YOLOv7

Due to the complexity and uncertainty of the chip manufacturing process, the problem of chip surface defects is becoming more and more serious, which seriously affects the product quality and production efficiency. Traditional detection methods are difficult to meet the requirements of high precision and real-time. Therefore, computer vision technology based on deep learning has become an important means to solve the problem of chip surface defect detection. As an advanced single-stage target detection algorithm, YOLOv7 has attracted widespread attention for its fast detection speed and high detection accuracy, but it still has limitations in the detection of micro defects on the chip surface and the adaptability to complex background. In this paper, a chip surface defect detection algorithm based on improved YOLOv7 is proposed. By introducing EMO (Efficient Model) network framework and LSK (Large Selective Kernel) attention mechanism, the backbone network structure of YOLOv7 is optimized. This improvement effectively reduces the number of parameters and computational complexity of the model, and significantly improves the detection accuracy and real-time performance. The experimental results show that the improved EMO-LSK-YOLOv7 algorithm performs better than original YOLOv7 and other lightweight variants on the self-built chip surface defect dataset. The algorithm provides an efficient and accurate solution for chip surface defect detection.