Erhu Zhang

Surface defect detection of lithium battery electrodes is vital for industrial automation but remains challenging due to scale variations and complex backgrounds. To address these challenges, this study proposes CEBW-YOLO11, an enhanced YOLO11n-based framework specifically designed for lithium battery electrode sheet defect detection. The proposed method integrates C3k2-DWR for multi-scale feature extraction, EAC for discriminative feature enhancement, BiFPN-DySample for adaptive feature fusion, and Wise-Inner-IoU for bounding box regression optimization. Experimental results on the electrode defect dataset demonstrate that, compared with the baseline YOLO11n, CEBW-YOLO11 improves Precision, Recall, and mAP@0.5 by 3.8, 4.7, and 3.8 percentage points, respectively. The proposed model achieves an inference speed of 90 FPS at an input resolution of 640 × 640 pixels, showing its potential for real-time electrode defect inspection applications.

One of the most significant problems affecting a concrete bridge’s safety is cracks. However, detecting concrete bridge cracks is still challenging due to their slender nature, low contrast, and background interference. The existing convolutional methods with square kernels struggle to capture crack features effectively, fail to perceive the long-range dependencies between crack regions, and have weak suppression ability for background noises, leading to low detection precision of bridge cracks. To address this problem, a multi-stage feature aggregation and structure awareness network (MFSA-Net) for pixel-level concrete bridge crack detection is proposed in this paper. Specifically, in the coding stage, a structure-aware convolution block is proposed by combining square convolution with strip convolution to perceive the linear structure of concrete bridge cracks. Square convolution is used to capture detailed local information. In contrast, strip convolution is employed to interact with the local features to establish the long-range dependence relationship between discrete crack regions. Unlike the self-attention mechanism, strip convolution also suppresses background interference near crack regions. Meanwhile, the feature attention fusion block is presented for fusing features from the encoder and decoder at the same stage, which can sharpen the edges of concrete bridge cracks. In order to fully utilize the shallow detail features and deep semantic features, the features from different stages are aggregated to obtain fine-grained segmentation results. The proposed MFSA-Net was trained and evaluated on the publicly available concrete bridge crack dataset and achieved average results of 73.74%, 77.04%, 75.30%, and 60.48% for precision, recall, F1 score, and IoU, respectively, on three typical sub-datasets, thus showing optimal performance in comparison with other existing methods. MFSA-Net also gained optimal performance on two publicly available concrete pavement crack datasets, thereby indicating its adaptability to crack detection across diverse scenarios.

Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the edge extraction module, guiding it into the denoising process through the attention mechanism, which maintains the edge detail of the restored inscription image. Moreover, a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains, which is helpful to blind denoising tasks. The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact. Due to the lack of a real-inscription image, we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising. The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.

In recent years, flexible continuum robots have been substantially developed. Absolute nodal coordinates formulation (ANCF) gives a feasible path for simulating the behavior of flexible robots. However, the model of finger-shaped robots is often regarded as a cylinder and characterized by a beam element. Obviously, this is short of characterizing the geometrical feature of fingers in detail, especially under bending conditions. Additionally, for the lower-order plate element, it is hard to characterize the bending behavior of the flexible finger due to fewer nodes; a higher-order plate element often requires an extremely long computing time. In this work, an improved ANCF lower-order plate element is used to increase the accuracy of the Yeoh model and characterize the geometrical structure of silicone rubber fingers, taking into particular consideration the effect of volume locks and multi-body system constraints. Since it is a kind of lower-order plate element, essentially, the computing time is nearly the same as that of conventional lower-order plate elements. The validity of this model was verified by comparing it with the results of the published reference. The flexible finger, manufactured using silicone rubber, is characterized by the novel ANCF lower-order plate element, whereby its mechanical deformation and bending behavior are simulated both efficiently and accurately. Compared to the ANCF beam element, conventional lower-order plate element, and higher-order plate element, the novel plate element in this paper characterizes the external contour of the finger better, reflects bending behavior more realistically, and converges in less computing time.

Fabric defect detection is a crucial problem in the production of textile industry. Nowadays, a large number of fabric defect detection algorithms based on deep learning have been proved to have excellent detection results, but the performance of detection often depends on large-scale labeled datasets. However, it is very laborious and costly to label a large number of pixel-level defect samples in industrial production. Although semisupervised detection method can reduce the labeling workload, fabric defect detection is still a challenging task due to the diversity of defects and the low contrast between defects and background. To address these issues, this article proposes a pixel-wise semisupervised fabric defect detection method combined with multitask mean teacher (MT). We focus on establishing a multitask detection model for defect area detection, defect contour detection, and defect distance map detection by leveraging their auxiliary information in better defect segmentation and assign this model as a student network and teacher network. Finally, the multitask supervised loss for labeled images and the multitask consistency loss for unlabeled images to train the network. Experiments on three public fabric defect detection datasets show that our method outperforms the current mainstream pixel-wise segmentation methods with MT, which verifies that the proposed network can effectively detect fabric defects by a small number of labeled samples.

The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.

Inverse halftoning acting as a special image restoration problem is an ill-posed problem. Although it has been studied in the last several decades, the existing solutions can’t restore fine details and texture accurately from halftone images. Recently, the attention mechanism has shown its powerful effects in many fields, such as image processing, pattern recognition and computer vision. However, it has not yet been used in inverse halftoning. To better solve the problem of detail restoration of inverse halftoning, this paper proposes a simple yet effective deep learning model combined with the attention mechanism, which can better guide the network to remove noise dot-patterns and restore image details, and improve the network adaptation ability. The whole model is designed in an end-to-end manner, including feature extraction stage and reconstruction stage. In the feature extraction stage, halftone image features are extracted and halftone noises are removed. The reconstruction stage is employed to restore continuous-tone images by fusing the feature information extracted in the first stage and the output of the residual channel attention block. In this stage, the attention block is firstly introduced to the field of inverse halftoning, which can make the network focus on informative features and further enhance the discriminative ability of the network. In addition, a multi-stage loss function is proposed to accelerate the network optimization, which is conducive to better reconstruction of the global image. To demonstrate the generalization performance of the network for different types of halftone images, the experiment results confirm that the network can restore six different types of halftone image well. Furthermore, experimental results show that our method outperforms the state-of-the-art methods, especially in the restoration of details and textures.