It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recognition accuracy of small foreign fibers, and low detection speed. A polarization imaging device of cotton foreign fiber was constructed based on the difference in optical properties and polarization characteristics between cotton fibers. An object detection and classification algorithm based on an improved YOLOv5 was proposed to achieve small foreign fiber recognition and classification. The methods were as follows: (1) The lightweight network Shufflenetv2 with the Hard-Swish activation function was used as the backbone feature extraction network to improve the detection speed and reduce the model volume. (2) The PANet network connection of YOLOv5 was modified to obtain a fine-grained feature map to improve the detection accuracy for small targets. (3) A CA attention module was added to the YOLOv5 network to increase the weight of the useful features while suppressing the weight of invalid features to improve the detection accuracy of foreign fiber targets. Moreover, we conducted ablation experiments on the improved strategy. The model volume, mAP@0.5, mAP@0.5:0.95, and FPS of the improved YOLOv5 were up to 0.75 MB, 96.9%, 59.9%, and 385 f/s, respectively, compared to YOLOv5, and the improved YOLOv5 increased by 1.03%, 7.13%, and 126.47%, respectively, which proves that the method can be applied to the vision system of an actual production line for cotton foreign fiber detection.

The paper proposed and verified a method of object edge detection based on the innovative defect spot working mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, edge-detection sensitivity could be improved. Calibration experiments with the piezoelectric transducer (PZT) and object edge-detection experiments indicate that the object edge-detection sensitivity and accuracy of our method could reach 1 and 20 nm, respectively. Therefore, this method can be widely used in high-precision alignment, geometric parameters measurement, and other fields.

The paper proposes and verifies a small-angle measurement method based on the defect spot mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, the measurement sensitivity can be significantly improved. Calibration experiments with the piezoelectric transducer (PZT) indicate that compared with the current PSD-based autocollimation method, the proposed method can improve the sensitivity of small-angle measurement by 57 times, and the measurement sensitivity of the proposed method can be further improved by optimizing the system parameters, while the proposed method has the advantages of a simple system and high real-time performance. Therefore, the proposed method is expected to be used in high-precision motion error detection, as well as in shape and position measurement.

To improve the calibration efficiency and accuracy of line structured light sensor, a plane checkerboard and concentric circle complementary line structured light calibration system was designed integrating with self-backlighting and pose-adjustable functions. Based on the geometric relationship between the real projection position of the concentric circle center and the center of projection ellipse, the nonlinear optimized compensation model for eccentric error was established, which precisely determined the eccentric error compensation position of the circle center under the perspective projection. Compared with the traditional calibration method, the proposed method reduced the re-projection error by 84.7% and effectively solved the problem of high-precision calibration for eccentric error compensation of circular markers. By combining the plane passing through the optical center and the center line of the light bar with the target plane in the camera coordinate system, the coordinate information of the spatial intersection lines was obtained for many times to increase the feature points, and the problem of lower calibration precision of plane fitting due to the fewer feature points was solved by using the least square method to fit the optic plane equation. In complex environments, the average error of the outer diameter of the large-size grinding wheel is 0.005 1 mm measured by repeated experiments, and the results show that the designed calibration system has certain precision, simplicity, and practicability.