Steel Surface Defect Detection Algorithm Based on ESI-YOLOv8

Abstract

Abstract To enhance the precision of detecting defects on steel plate surfaces and diminish the incidences of false detection and leakage, the ESI-YOLOv8 algorithm is introduced. The algorithm incorporates an EP module, which minimizes redundant computations and model parameters to optimize efficiency and simultaneously increases the multi-scale fusion mechanism to expand the sensory field. The SPPF-LSKA module is proposed by integrating the large separated convolutional attention module with the spatial pyramid pooling module. This integration reduces computational complexity, accelerates model operation speed, and improves detection accuracy. Additionally, the INNER-CIOU loss function is introduced to improve detection speed and model accuracy by controlling the scale size of the auxiliary border. The results of the experiment indicate that, following the improvements made, the algorithm's detection accuracy has increased to 78%, which is 3.7% higher than the original YOLOv8. Furthermore, the model parameters were reduced, and the verification was conducted using the CoCo dataset, resulting in an average accuracy of 77.8%. In conclusion, the algorithm has demonstrated its ability to perform steel plate surface defect detection with efficiency and accuracy.