YOLOv8-VSC: Lightweight Algorithm for Strip Surface Defect Detection

Abstract

Currently, in the field of strip steel surface defect detection, the generalized target detection algorithm is highly complex and computationally large, while terminal equipment responsible for the detection of some small and medium-sized enterprises usually does not have strong computational capabilities, and the computational resources are limited, which leads to difficulties in the deployment of detection algorithms. To solve this problem, this paper proposes a lightweight strip steel surface defect detection model YOLOv8-VSC based on the YOLOv8n target detec-tion framework, which uses a lightweight VanillaNet network as the backbone feature extraction network and reduces the complexity of the model by reducing the unnecessary branching structure. Meanwhile, the SPD module is introduced to speed up the inference of the model while reducing the number of network layers. To further improve the detection accuracy, a lightweight up-sampling operator, CARAFE, is used in the feature fusion network to improve the quality and richness of the features. Finally, extensive experiments on the NEU-DET dataset yield a model with parametric and computational quantities of 1.96×106 and 6.0 GFLOPs, which are only 65.1% and 74.1% of the baseline, and the mAP reaches 80.8%, which is an improvement of 1.8 percentage points from the baseline. In addition, experimental results on the aluminum surface defect dataset and the VOC2012 dataset show that the proposed algorithm has good robustness. Compared with advanced target detection algorithms, the proposed algorithm requires fewer computational resources while ensuring high detection accuracy.

Keywords

• defect detection; strip surface defect; yolov8; lightweight network; vanillanet