This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

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UAV multitarget detection plays a pivotal role in civil and military fields. Although deep learning methods provide a more effective solution to this task, changes in target size, shape change, occlusion, and lighting conditions from the perspective of drones still bring great challenges to research in this field. Based on the above problems, this paper proposes an aerial image detection model with excellent performance and strong robustness. First, in view of the common problem that small targets in aerial images are prone to misdetection and missed detection, the idea of Bi-PAN-FPN is introduced to improve the neck part in YOLOv8-s. By fully considering and reusing multiscale features, a more advanced and complete feature fusion process is achieved while maintaining the parameter cost as much as possible. Second, the GhostblockV2 structure is used in the backbone of the benchmark model to replace part of the C2f module, which suppresses information loss during long-distance feature transmission while significantly reducing the number of model parameters; finally, WiseIoU loss is used as bounding box regression loss, combined with a dynamic nonmonotonic focusing mechanism, and the quality of anchor boxes is evaluated by using “outlier” so that the detector takes into account different quality anchor boxes to improve the overall performance of the detection task. The algorithm’s performance is compared and evaluated on the VisDrone2019 dataset, which is widely used worldwide, and a detailed ablation experiment, contrast experiment, interpretability experiment, and self-built dataset experiment are designed to verify the effectiveness and feasibility of the proposed model. The results show that the proposed aerial image detection model has achieved obvious results and advantages in various experiments, which provides a new idea for the deployment of deep learning in the field of UAV multitarget detection.

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

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