Aluminum Product Surface Defect Detection Method Based on Improved CenterNet

Abstract

In order to realize real-time detection of aluminum defects during aluminum production, the target detection algorithm needs to be able to run on locally deployed hardware. Convolutional neural networks can effectively extract representative features from high-dimensional data such as images and videos, and capture spatial information in the data, making it easier to locate aluminum defects. Moreover, running CNN model inference on local hardware has high real-time performance. Due to the advantages of convolutional neural network in anomaly detection, an improved CenterNet aluminum surface defect detection method was proposed. The algorithm combines common convolution and depthwise separable convolution to design a lightweight convolution module. Then, the Convolutional Block Attention Module is added to the feature extraction network to make the network better capture the rich input feature information of the image. Ultimately, the α-DIoU loss function is implemented to enhance the precision of bounding box predictions. The experimental findings demonstrate that the proposed algorithm achieves an average detection accuracy (mAP) of 86.02%, which is 5.95% higher than the average detection accuracy of the traditional algorithm, and has a good detection effect on aluminum surface defects. Furthermore, there is an 11.9% reduction in model parameters and a 15.2% decrease in floating-point computations, which helps to promote the deployment of embedded device platforms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.