An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5.

Limited by computing resources of embedded devices, there are problems in the field of fabric defect detection, including small defect size, extremely unbalanced aspect ratio of defect size, and slow detection speed. To address these problems, a sliding window multihead self-attention mechanism is proposed for the detection of small targets, and the Swin Transformer module is introduced to replace the main module in the original YOLOv5 algorithm. First, to reduce the distance between several scales, the weighted bidirectional feature network is employed on embedded devices. In addition, it is helpful to improve the perception of small-target faults by incorporating a detection layer to achieve four-scale detection. At last, to improve the learning of positive sample instances and lower the missed detection rate, the generalized focal loss function is finally implemented on YOLOv5. Experimental results show that the accuracy of the improved algorithm on the fabric dataset reaches 85.6%, and the mAP is increased by 4.2% to 76.5%, which meets the requirements for real-time detection on embedded devices.

[A mechanical impedance-based measurement system for quantifying Parkinsonian rigidity].

At present the parkinsonian rigidity assessment depends on subjective judgment of neurologists according to their experience. This study presents a parkinsonian rigidity quantification system based on the electromechanical driving device and mechanical impedance measurement method. The quantification system applies the electromechanical driving device to perform the rigidity clinical assessment tasks (flexion-extension movements) in Parkinson's disease (PD) patients, which captures their motion and biomechanical information synchronously. Qualified rigidity features were obtained through statistical analysis method such as least-squares parameter estimation. By comparing the judgments from both the parkinsonian rigidity quantification system and neurologists, correlation analysis was performed to find the optimal quantitative feature. Clinical experiments showed that the mechanical impedance has the best correlation (Pearson correlation coefficient r = 0.872, P < 0.001) with the clinical unified Parkinson's disease rating scale (UPDRS) rigidity score. Results confirmed that this measurement system is capable of quantifying parkinsonian rigidity with advantages of simple operation and effective assessment. In addition, the mechanical impedance can be adopted to help doctors to diagnose and monitor parkinsonian rigidity objectively and accurately.