In industrial aluminum sheet surface defect detection, false detection, missed detection, and low efficiency are prevalent challenges. Therefore, this paper introduces an improved YOLOv8 algorithm to address these issues. Specifically, the C2f-DSConv module incorporated enhances the network’s feature extraction capabilities, and a small target detection layer (160 × 160) improves the recognition of small targets. Besides, the DyHead dynamic detection head augments target representation, and MPDIoU replaces the regression loss function to refine detection accuracy. The improved algorithm is named YOLOv8n-DSDM, with experimental evaluations on an industrial aluminum sheet surface defect dataset demonstrating its effectiveness. YOLOv8n-DSDM achieves an average mean average precision (mAP50%) of 94.7%, demonstrating a 3.5% improvement over the original YOLOv8n. With a single-frame detection time of 2.5 ms and a parameter count of 3.77 M, YOLOv8n-DSDM meets the real-time detection requirements for industrial applications.

To address the issues of low search efficiency, excessive node expansion, and the presence of redundant nodes in the traditional A* algorithm, this article proposes an improved A* algorithm for mobile robot path planning. Firstly, a multi-neighborhood hybrid search method is introduced, optimizing the traditional eight-neighborhood and twenty-four-neighborhood into a new sixteen-neighborhood. The choice between eight-neighborhood search and sixteen-neighborhood search is determined based on the presence of obstacles in the eight-neighborhood around the current node, effectively enhancing the search efficiency of the algorithm and reducing the number of nodes expanded during the search process. Subsequently, unnecessary nodes are eliminated based on the positional relationship between the current node and the target node, according to neighborhood direction search rules, further decreasing the number of expanded nodes. Additionally, improvements to the bidirectional search mechanism along with the incorporation of dynamic weight coefficients further enhance the search efficiency of the algorithm. Furthermore, a strategy for extracting key nodes is employed to effectively remove useless turn points, thus resolving the issue of redundant nodes. Finally, simulation experiments demonstrate that the proposed improved A* algorithm outperforms the traditional A* algorithm in terms of search speed, number of expanded nodes, and path length, validating the effectiveness of the proposed method.

Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs.

Calligraphy works have high artistic value, but there is the rampant problem of forgery. Indeed, the authentication of traditional calligraphy heavily relies on calligraphers’ subjective judgment. Therefore, spurred by the recent development of neural networks, this paper proposes a method for authenticating calligraphy works based on an improved EfficientNet network. Specifically, the developed method utilizes the character box algorithm to efficiently extract individual calligraphy characters, which are then augmented and used as the training set for the model. The training process employs CBAM and Self-Attention modules to enhance the attention mechanism of the EfficientNet network. The trained network model is used to judge the calligraphy works’ similarity; tested on authentic works, imitated works, and works from other calligraphers; and compared with other networks. The experimental results demonstrate that the proposed method effectively achieves the authentication of calligraphy works, and the improved CBAM-EfficientNet network and SA-EfficientNet network achieve better authentication performance.

Efficient and stable control and task assignment optimization in electronic commerce logistics and warehousing systems involving multiple robots executing multiple tasks is highly challenging. Hence, this paper proposes a Swiss round selection algorithm for multi-robot task allocation to address the challenges mentioned. Firstly, based on the shipping process of electronic commerce logistics and warehousing systems, the tasks are divided into packaging and sorting stages, and a grid model for the electronic commerce warehousing system is established. Secondly, by increasing the probabilities of crossover and mutation in the population and adopting a full crossover and full mutation approach, the search scope of the population is expanded. Then, a Swiss round selection mechanism with burst probability is proposed, which ensures the smooth inheritance of high-quality individuals while improving the diversity of the population. Finally, 12 comparative experiments are designed with different numbers of robots and tasks. The experimental results demonstrate that the Swiss round selection algorithm outperforms the genetic algorithm in terms of maximum task completion time and convergence time to reach the optimal value. Thus, the effectiveness of the Swiss round selection algorithm in solving the multi-robot task allocation problem is verified.

Due to imperfect manufacturing processes and external factors, aluminum profiles can exhibit various surface defects, which significantly affect their service life. To address the issues of low accuracy and high computational load in current aluminum profile surface defect detection methods, this paper proposes a high-precision and lightweight aluminum profile surface defect detection model, YOLOv8-ARL. The model first replaces some Conv modules in the backbone and neck with an Adown down-sampling module, which reduces the number of parameters while retaining more image information. Next, a lightweight module, RGCSPELAN (RepGhostCSPELAN), is introduced to replace the C2f module in the network, enhancing gradient flow information while improving computational resource utilization. Finally, a lightweight detection head is constructed using the concept of shared convolution, replacing the BN (Batch Normalization) layers in the Conv modules to ensure that lightweight design does not compromise accuracy. The improved YOLOv8-ARL algorithm achieves an average mean precision (mAP50%) of 88.6% on the APDDD aluminum profile dataset, a 2.2% increase over the original YOLOv8s model, with a parameter count of 4.55M, reduced by 59% compared to the original model, and a computational load reduced by 48%. The detection time per image is 2.2ms. Experimental results demonstrate that the improved YOLOv8-ARL model maintains excellent detection accuracy and speed while being lightweight.