Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://github.com/TongkunGuan/RFN .

Low-light image enhancement is a crucial visual task, and many unsupervised methods overlook the degradation of visible information in low-light scenes, adversely affecting the fusion of complementary information and hindering the generation of satisfactory results. To address this, we introduce Wakeup-Darkness, a multimodal enhancement framework that innovatively enriches user interaction through voice and textual commands. This approach signifies a technical leap and represents a paradigm shift in user engagement. We introduce a Cross-Modal Feature Fusion (CMFF) that synergizes semantic and depth context with low-light enhancement operations. Moreover, we propose a Gated Residual Block (GRB) and a channel-aware Look-Up Table (LUT) to adjust the intensity distribution of each channel. Crucially, the proposed Wakeup-Darkness scheme demonstrates remarkable generalization in unsupervised scenarios. The source code can be accessed from https://github.com/zhangbaijin/Wakeup-Dakness .

The condition monitoring is essential to the advanced manufacturing process in the era of the fourth industrial revolution because it ensures the prediction and optimization of machine tool conditions via data analytics or physical modeling methods. The cyber-physical system (CPS) has the property of intellectuality, scalability, adaptability, and openness, making it suitable for machine tool monitoring. The current data-driven CPS method is prone to interpretability and generalization limitations due to the empirical selection of hyper-parameters in the model and the need for heterogeneous data. On the other hand, traditional model-driven systems are difficult to adjust models to practical working conditions data due to empirical equations constructed by offline data. This article proposes a novel model-driven cyber-physical system (MDCPS) to overcome these weaknesses. First, the physical model generates a counterpart of the machining process to form a cyber world, and sensors depict the real-time state of the machining process to form a physical world. Second, for deep fusion between the cyber and physical worlds, the extended Kalman filter (EKF) approach is applied to calibrate the empirical model with online measured data. Third, the model-based diagnosis and prediction methods are used for online monitoring and control. Case studies of MDCPS for machining monitoring are presented to prove the feasibility of this model-driven system.