Convolutional neural networks (CNN) have achieved promising performance in surface defect detection recently. Although many CNN-based methods have been proposed, most of them are limited by the few samples available for training, and the imbalance of positive and negative samples. Hence, their detection performance needs to be further improved. To this end, we propose a multi-scale cascade CNN called MobileNet-v2-dense to detect defects more efficiently. Specifically, the multi-scale cascade structure used in our network can help capture the weak defect semantics that may be lost in the deep network. Then, we propose a novel asymmetric loss function to further improve detection performance. Lastly, a two-stage augmentation method effectively enlarges the training dataset. Experimental results show that, compared to the state-of-the-art, the area under the receiver-operating characteristic curve (AUC-ROC) score of our method increased by 0.16.

In inverted pendulum visual servo control system (IPVSCSs), how to achieve the fast and effective measurement of the state information of the inverted pendulum is an important task. In this paper, the characteristics of the inverted pendulum are considered and a series of image processing and positioning algorithm of the pendulum are proposed. Furthermore, the inverted pendulum control system model based on the pixel displacement is established and the LQR controller is designed. Finally, the feasibility and superiority of the proposed method are verified by real-time control experiments.

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