Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach

Abstract

Steel strip can develop surface defects during manufacturing and processing, affecting structural integrity and usability. These defects can be caused by both internal and external factors. However, traditional manual error detection techniques do not meet today's accuracy standards. Therefore, an improved version of the YOLOv7 algorithm for steel strip surface defect detection is proposed in this work. A lightweight and inexpensive Coordinate Attention (CA) mechanism is built into the structure of the head of YOLOv7. The SCYLLA-Intersection over Union (SIoU) loss function is used to improve detection efficiency. Furthermore, to enhance the dataset, a vertical flip augmentation technique is applied to create the optimal model:YOLOv7-CSF through fusion of CA and SIoU. It has been observed in the experimental findings that the modified YOLOv7-CSF algorithm's mAP value in the detection is 4.09% better than that of the original YOLOv7 method, reaching 66.1% and a maximum of 96.9% accuracy in a single category of defects. The efficacy and superiority of the updated model are shown by comparing it with the recently announced YOLOv8, other steel strip datasets and other hyperparameter tuned models, providing a novel way for daily surface defect detection on steel strips.

DCGC-YOLO: The Efficient Dual-Channel Bottleneck Structure YOLO Detection Algorithm for Fire Detection

“…Compared with traditional sensor-based methods, visual-based fire detection technology has the following advantages: High sensitivity, Visualization, High precision, Low cost, High flexibility. Deepti Raj, G et al [5] [6]. improved YOLOv7 and improved the detection accuracy of Steel Tube and Steel Strip.…”

Steel Surface Defect Detection Algorithm Based on YOLOv8

“…The training strategy of convolution kernel of different sizes corresponding to feature maps of different scales is adopted to adapt to defects of different shapes. Raj et al [28] proposed the YOLOV7-CSF model, which introduced a lightweight and low-cost coordinate attention mechanism into the head structure of YOLOv7, then adopted SCYLLA-Intersection over Union loss function to improve detection efficiency. Huang et al [29] proposed the WFE-YOLOv8s model based on YOLOv8s, replacing the original C2F module with a new CFN structure, reducing the number of network parameters and GFLOPs, and improving the algorithm accuracy through an EMA attention mechanism, which increased by 4.7 percentage points compared with the mAP of the original model.…”