An overview of industrial image segmentation using deep learning models

Abstract

Image segmentation plays a vital role in artificial intelligence and computer vision with major applications such as industrial picking, defect detection, scene understanding and video surveillance. As parallel computing technologies develop, numerous deep learning (DL)-based segmentation algorithms have demonstrated practical performance with increased efficiency and accuracy. With the concept of DL image segmentation, a comprehensive review on recent literature is introduced in detail, including traditional image segmentation algorithms, DL schemes and the fusion of the former two algorithms. The seminal efforts of DL in image segmentation are elaborated in accordance with the quantity and quality of annotated labels, covering supervised, weakly-supervised, and unsupervised frameworks. Numerous methods on industrial benchmark datasets are compared and analyzed in standard evaluation indicators. Finally, the challenges and opportunities of DL image segmentation are discussed for further research.