Online surface defect segmentation on aluminum strip production line using a lightweight and efficient model

Abstract

A significant challenge in automated defect inspection (ADI) of aluminum strip surfaces is improving segmentation speed to satisfy the online inspection requirements of the production line while maintaining the precision of defect identification. This study proposes a lightweight and efficient defect segmentation model that can be applied in aluminum processing enterprises for fast and precise segmentation of aluminum strip surface defects. A novel fusion attention (FA) mechanism is first established to enhance the focus on critical characteristics along the spatial and channel dimensions. This mechanism adopts continuous dilated convolutions with appropriate dilation rates to effectively increase the range of the receptive field and improve defect localization accuracy. Subsequently, a lightweight MobileViTv2 with an embedded FA mechanism is employed as a multi-scale feature extractor to learn comprehensive representations from defect images. Next, a novel feature fusion method, named large-scale feature pyramid network (LSFPN), is introduced to enhance the focus on details within large-scale features. LSFPN establishes four progressively shallower top-down pathways with fast normalized fusion weights and incorporates lightweight aggregation nodes based on the MoblieNetv2 block. Surface images of straightened aluminum strips with five universal defects were collected, whereby a new dataset was established. The experimental outcomes demonstrate the proposed model outperforms other state-of-the-art techniques synthetically, achieving a mean Intersection over Union (mIoU) of 87.01%, a segmentation speed of 61.67 fps, and a model size of 16.23 MB. This model may serve as a valuable theoretical foundation for the online segmentation of aluminum strip surface defects in embedded devices.