PIC2f-YOLO: a lightweight method for the detection of metal surface defects

Metallic materials, especially steel, are widely used in industry due to their mature manufacturing processes, excellent durability, and significant economic benefits. As a core material in various manufacturing sectors, steel is critical in advancing global industrialization and information technology. In the early stages of industrial metal production, surface defect detection relied primarily on manual visual inspections. However, as production scales expanded and quality demands increased, the limitations of manual inspection methods, such as inefficiency, susceptibility to human error, and high subjectivity, became evident. Thus, quickly and reliably identifying surface defects on metal has become a critical challenge in modern manufacturing processes. Traditional nondestructive testing techniques, such as eddy current testing and infrared inspection, were initially applied to surface defect detection on metal materials. However, these techniques often struggle to detect small defects effectively. Later, machine learning methods were introduced to improve detection accuracy, but limitations in processing power and generalization hindered their performance in large-scale industrial applications. With the rapid development of computing hardware, deep-learning-based methods have become the primary solution for metal surface defect detection. These methods are usually divided into two-stage and one-stage object detection frameworks. Two-stage methods, such as Faster RCNN, are recognized for their high accuracy but have slower processing speeds, limiting their real-time applications. Conversely, one-stage methods, such as YOLO, provide faster detection speeds but often compromise accuracy. To address the low efficiency in metal surface defect detection, as well as problems related to large algorithm parameters and low precision, this paper proposes a lightweight detection method based on the improved YOLOv8n. First, the local convolution inverted cross-stage partial fusion (PIC2f) module was designed. It replaces the BottleNeck module with the constructed local convolution inverted residual bottleneck (PIBN) module, which combines partial convolution and an inverted residual block to reduce algorithm parameters and enhance the model's feature extraction ability. Next, an attention-based intra-scale feature interaction (AIFI) module was adopted. It combines position embedding and multi-head attention to enhance the model's detection capability for small targets. Lastly, average pooling downsampling (ADown) replaced traditional convolution as the feature reduction module, effectively reducing parameters and computational complexity without compromising detection accuracy. Experimental results show that, compared with YOLOv8n, the PIC2f-YOLO method increases mAP₅₀ by 2.7 % on the NEU-DET steel defect dataset and reduces parameters by 0.403 M. Experiments on aluminum sheet surface industrial defects, PASCAL VOC2012, and surface defects of strip alloy functional material datasets also confirm the effectiveness of the PIC2f-YOLO method.