Fast and efficient computing for deep learning-based defect detection models in lightweight devices

Defect anomaly detection is beneficial in the production cycle of various industries. It is widely used in areas such as metal surface and fabric industries. This paper focuses on deep learning-driven defect detection models using energy-efficient computing. We concentrate on a segmentation-based defect detection model for metal surface anomaly detection, while we deal with a deconvolution-based defect detection model for fabric defects in this work. We propose a depth-wise convolution structure for the segmentation-based visual defect detection model. In addition, we apply the optimizations supported by the inference engine to two models. The segmentation-based defect detection model inference is approximately 10 $$\times $$ × faster than the original. Furthermore, the real-time requirement is achieved in a lightweight vision processing unit (VPU) device with a power consumption of only 1.5 Watts for the fabric defect detection model. The practical values of this work are multifaceted, offering substantial benefits in terms of cost reduction, product quality, real-time processing, energy efficiency, and scalability. These advancements not only improve operational efficiency but also contribute to sustainability efforts and provide a competitive advantage in the industry.