# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
This advancement has significantly improved detection accuracy, robustness, and efficiency, making deep learning an essential tool in industrial applications for surface defect detection. The YOLO series, with its high efficiency in detecting the target, provides a more efficient and accurate detection technique for strip surface defect detection. [PERSON] et al. [18] proposed a YOLOv5s-CSG model outperformed the original YOLOv5s by 7.3%. [PERSON] et al. [19] proposed an adaptive focusing function AFF algorithm on the NEU-DET data set, achieving an average accuracy of 83.5%. [PERSON] et al. [20] improved a YOLOX-ADS model, introducing the spatial depth (SPD) layer to prevent the loss of fine-grained information for small target features, and the improved SPPFCSPC module enhances the model's fusion and representation ability. The mean precision and mean average precision of this method on the NEU-DET dataset were 81.4% and 79.9%. In [21], a slim-YOLOv8 model was proposed, then the online reparameterization method was introduced to reduce the number of model parameters and was finally tested on the NEU-DET dataset. Compared with YOLOv8, the accuracy of IoU 0.5 was increased by 8.3%, and the number of model parameters was reduced from 3.0 M to 2.7 M. [PERSON], [PERSON] et al. [22] investigated an enhanced lightweight YOLOv7 model and obtained a 4.5% improvement in mAP50 over YOLOv7. In [23], GSConv was used to design an improved and efficient aggregation network by improving YOLOv7-tiny by adding SPDConv modules for low resolution and small defects between the prediction head and neck networks, with an average accuracy mean of up to 74.1%. In [PERSON] et al. [24], an ECA-SimSPPF-SIoU-Yolov5-based algorithm achieved good detection results in mean average precision and recall. In [25], a FANet fusion attention network was proposed for detecting various steel surface defects by changing the backbone of FANet to ResNet-50, and a higher detection speed was achieved. In [26], a real-time detection network (RDN) was proposed that balanced speed and accuracy achieved 80.0% mAP, while [PERSON] et al. [27], based on the improved YOLOX algorithm, achieved 77% mAP on the NEU-DET dataset with a speed of 100 FPS. The above methods have been improved and designed based on the YOLO series. Nevertheless, steel strip surface defect detection still faces several challenges. First, due to the wide variety of defects and their variations in different production environments, automating defect identification and classification remains a challenge, particularly with regard to data diversity and quality. Second, surface defects often resemble the surrounding environment and the texture of the metal, making it difficult for models to detect subtle defects and accurately differentiate between surface textures and defects. Lastly, ensuring high detection accuracy while improving the system's real-time performance and computational efficiency--especially in large-scale data processing and high-speed production environments--remains an important direction for future research. In summary, steel strip surface defect detection, particularly automation methods based on machine learning and deep learning, has become an essential part of modern steel manufacturing. In the future, with continuous algorithm optimization and the expansion of datasets, steel strip defect detection will become even more intelligent and efficient, further advancing the automation and intelligence of the steel industry. Here, we propose a YOLO-BSPB model aimed at improving the precision of surface defect detection in steel strips. The contributions of this paper are as follows:  
1. To address the information loss caused by the SPPF pooling layer in YOLOv8 during the feature extraction process, we replaced it with an enhanced SPPCSPC [28] module. This improvement includes substituting max pooling with average pooling and integrating DCNv4 [29] to dynamically adjust convolution operations, enabling the capture of diverse feature information.