Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5

Abstract

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 385f/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.

Study on the Detection Mechanism of Multi-Class Foreign Fiber under Semi-Supervised Learning

“…To further verify the detection performance of the improved model, Efficient YOLOv5cotton, in foreign fiber detection, we conduct comparative experiments with YOLOv3 [28], YOLOv5 [8], YOLOv7 [29], Faster-RCNN [30], and DETR [31]. We also perform experiments under two sizes of the training set to better reflect the effectiveness of the semi-supervised foreign fiber detection algorithm in this paper: (1) The same 10% foreign fiber training set (336 labeled images) used in the Efficient YOLOv5-cotton experiment; (2) The entire foreign fiber training set (3360 labeled images).…”

“…Rapid manual sorting makes it challenging to distinguish foreign fibers from cotton accurately, resulting in slow detection work and increased production time costs. In recent years, the rapid development of artificial intelligence technology, particularly deep learning, has led to significant advancements in foreign fiber detection methods [4][5][6][7][8]. Xuehua Zhao [4] used the feature selection method to match classifiers and select the optimal feature set for detecting foreign fibers, obtaining excellent performance in foreign fiber detection with Extreme Learning Machine and Kernel Support Vector Machine, which achieved classification accuracies of 93.61% and 93.17% respectively, using feature sets of 42 and 52 features.…”

“…Wei Wei [7] proposed a foreign fiber classification model "CottonNet-Fusion" based on a residual network with feature difference fitting for the problem that foreign fibers in raw cotton are similar to cotton in terms of features, which maintains the classification accuracy at 90.3% in the complex environment sampled images. Rui Wang [8] proposed an improved object detection and classification algorithm based on the optical and polarization differences between cotton fibers, which achieves the recognition and classification of small foreign fibers with an average identification accuracy of 96.9%. These methods use the advantages of handling large data samples and have successfully detected foreign fibers.…”

A linearly polarized light emission with a composite nanowire grating in whole white band

“…Polarization is a fundamental property of light, which has been applied to visual imaging field for a long history. Especially in the current rapid development of machine vision, polarized light is selected to overcome the excess light, reflection, haze, and glare produced from reflective object surfaces [1][2][3][4]. The polarized white light with a high extinction ratio is particularly important in vision systems to obtain images with good uniformity, high contrast and clear legibility [5].…”

Extraction of the Microstructure of Wool Fabrics Based on Structure Tensor

“…With the widening application of computer vision in the textile industry [ 12 , 13 , 14 ], it is more convenient to process the high-definition top view images of fabric for feature extraction. Luan et al [ 15 ] used the polarization properties of yarns for accurate segmentation of the warps and wefts, overcoming the requirement of other methods regarding the number of colors contained in the textile and allowing the processing of fabrics consisting of only two colors.…”