Search

Segmentation of weak edge targets such as glass and plastic poses a challenge in the field of target segmentation. The detection process is susceptible to background interference and various external factors due to the transparent nature of these materials. To address this issue, this paper introduces a segmentation network for weak edge target objects (WETS-Net). To effectively extract edge information of such objects and eliminate redundant information during feature extraction, a dual-attention mechanism is employed, including the Edge Attention Extraction Module (EAEM) and the Multi-Scale Information Fusion Module (MIFM). Specifically, the EAEM combines improved edge feature extraction kernels to selectively enhance the importance of edge features, aiding in more precise target region extraction. The MIFM utilizes spatial attention mechanisms to fuse multi-scale features, reducing background and external interference. These innovations enhance the performance of WETS-Net, offering a new direction for weak edge target segmentation research. Finally, through ablation experiments, the effectiveness of each module is effectively validated. Moreover, the proposed algorithm achieves an average detection accuracy of 95.83% and 96.13% on the dataset and a self-made dataset, respectively, outperforming similar U-Net-improved networks. [ABSTRACT FROM AUTHOR]

Achieving vibration-free smooth motion of industrial robotic arms in a short period is an important research topic. Existing path planning algorithms often sacrifice smoothness in pursuit of efficient motion. A robotic trajectory planning particle swarm optimization algorithm (RTPPSO) is introduced for optimizing joint angles or paths of mechanical arm movements. The RTPPSO algorithm is enhanced through the introduction of adaptive weight strategies and random perturbation terms. Subsequently, the RTPPSO algorithm is utilized to plan selected parameters of an S-shaped velocity profile, iterating to obtain the optimal solution. Experimental results demonstrate that this velocity planning algorithm significantly improves the acceleration of the robotic arm, surpassing traditional trial-and-error velocity planning methods. [ABSTRACT FROM AUTHOR]

The exponential increase in waste generation is a significant global challenge with serious implications. Addressing this issue necessitates the enhancement of waste management processes. This study introduces a method that improves waste separation by integrating learning models at various levels. The method begins with the creation of image features as a new matrix using the Multi-Scale Local Binary Pattern (MLBP) technique. This technique optimally represents features and patterns across different scales. Following this, an ensemble model at the first level merges two Convolutional Neural Network (CNN) models, with each model performing the detection operation independently. A second-level CNN model is then employed to obtain the final output. This model uses the information from the first-level models and combines these features to perform a more accurate detection. The study's novelty lies in the use of a second-level CNN model in the proposed ensemble system for fusing the results obtained from the first level, replacing conventional methods such as voting and averaging. Additionally, the study employs an MLBP feature selection approach for a more accurate description of the HW image features. It uses the Simulated Annealing (SA) algorithm for fine-tuning the hyperparameters of the CNN models, thereby optimizing the system's performance. Based on the accuracy metric, the proposed method achieved an accuracy of 99.01% on the TrashNet dataset and 99.41% on the HGCD dataset. These results indicate a minimum improvement of 0.48% and 0.36%, respectively, compared to the other methods evaluated in this study. [ABSTRACT FROM AUTHOR]

Early screening for cervical cancer is a common form of cancer prevention. In the microscopic images of cervical cells, the number of abnormal cells is small, and some abnormal cells are heavily stacked. How to solve the segmentation of highly overlapping cells and realize the identification of single cells from overlapping cells is still a heavy task. Therefore, this paper proposes an object detection algorithm of Cell_yolo to effectively and accurately segment overlapping cells. Cell_yolo adopts a simplified network structure and improves the maximum pooling operation, so that the information of the image is preserved to the greatest extent during the model pooling process. Aiming at the characteristics of many overlapping cells in cervical cell images, a non-maximum suppression method of center distance is proposed to prevent the overlapping cell detection frame from being deleted by mistake. At the same time, the loss function is improved and the focus loss function is added to alleviate the imbalance of positive and negative samples in the training process. Experiments are conducted on a private dataset (BJTUCELL). Experiments have verified that the Cell_yolo model has the advantages of low computational complexity and high detection accuracy, and it is superior to common network models such as YOLOv4 and Faster_RCNN.