ESI-YOLOv11n: Efficient Multi-Scale Fusion Method for PCB Defect Detection

The printed circuit board (PCB), a core component of electronic products, is playing an increasingly critical role in quality defect detection. Traditional methods suffer from low efficiency and high missed detection rates, rendering them insufficient to meet the industrial requirements for PCB defect detection. To address this issue, this paper proposes an ESI-YOLOv11n model for PCB defect detection that incorporates multi-scale feature fusion. The specific improvements are as follows: First, Spatial and Channel Reconstruction Convolution (ScConv) is incorporated to optimize the C3k2 module, creating a dynamic adaptive feature extraction unit that strengthens its ability to capture features of small defects. Second, an Efficient Multi-Scale Attention (EMA) mechanism is integrated into the Neck layer, dynamically adjusting the weight distribution of multi-scale feature maps to enhance efficiency of feature fusion and improve detection performance. Finally, the Inner concept is integrated with the CIoU loss function, resulting in the novel Inner-CIoU loss function. This loss function optimizes the model by utilizing auxiliary box mechanisms and geometric constraints, leading to more accurate regression results. Experimental results show that the improved model achieves an average precision of 95.9% and a recall rate of 93.3%, which are 9.3% and 11.5% higher than those of the original model, respectively, while having a parameter size of only 13.3 Mb. The model effectively reduces the missed detection rate and false detection rate, significantly enhances the PCB defect detection performance, and demonstrates superior comprehensive performance compared with current mainstream detection models.