LAUSR

As a critical component of power battery, the quality of nickel-plated punched steel strip (NPSS) is closely related to the performance of battery. In practice, however, the real-time detection for defects of NPSS with limited computating resources is a challenging task. Current research on vision-based strip defect detectors emphasizes the use of large neural network models to pursue high accuracy while ignoring the training cost and hardware requirements. To accelerate the model update and deployment cycles and reduce the application cost, a lightweight detection network for accurate and fast detection of surface defects in NPSS is developed in this article. First, a new lightweight backbone network, lightweight efficient detection network (LEDNet), is designed to extract features. Depth dynamic convolution is used as the basic structure of the network to reduce the computational complexity while adaptively extracting effective features. Thereafter, a refine-residual bidirectional aggregation network is built to enhance the bidirectional propagation and reuse high- and low-level features by further optimizing and adjusting the extracted features. On this basis, feature aggregation capability is improved. Finally, the intersection-over-union k-means algorithm is used to adjust the anchor box size, which balances the significant difference in aspect ratio of surface defects of NPSS. Experimental results on the NPSS defect dataset show that our network detection accuracy can reach 87.17%, the single-sheet detection rate is 0.09 s, and the computational complexity is reduced. Compared with the state-of-the-art detectors, it achieves a strong competitive accuracy and low computational effort, which fully meets the accuracy and real-time requirements in the production environment of NPSS.