Qinggang Meng

TL;DR: This paper proposed a novel defect detection system based on deep learning and focused on a practical industrial application: steel plate defect inspection and employs a baseline convolution neural network to generate feature maps at each stage, and the proposed multilevel feature fusion network (MFN) combines multiple hierarchical features into one feature, which can include more location details of defects.

TL;DR: A pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net, which outperforms the state-of-the-art methods on mean intersection of union and mean pixel accuracy.

TL;DR: This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption and shows that the results improve and generalize the results derived in the previous literature.

TL;DR: A novel heuristic distributed task allocation method for multivehicle multitask assignment problems that is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm.

TL;DR: The previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis, and event reconstruction are reviewed.