An Online Quality Detection Algorithm for Cocoon Clusters Based on CD-YOLO
Year: 2024
Type: article
Abstract: Silk cocoons are an important raw material for textiles. The mixture of defective cocoons, such as thin-shelled cocoons, double-pupa cocoons, and yellow-spotted cocoons, can lead to issues during the reeling process, such as thread breakage, uneven material, or contamination, which diminishes the quality of the silk. This paper addresses the cocoon sorting problem by leveraging the image characteristics of defective cocoons and proposes a CD-YOLO defect classification and recognition method based on YOLOv7. First, an image acquisition system based on a line array camera is established to achieve efficient and high-quality cocoon image collection. Data augmentation is applied to the sample images, and a cocoon dataset is created. Then, using the structural characteristics of a grid-clustered layout, the original cocoon images are pre-segmented, and the K-Canopy algorithm is used for clustering to accurately obtain anchor boxes that match the cocoon size and complete the segmentation of individual cocoon images. Next, model improvements are made by adding a CBAM module to the backbone network, incorporating the SE attention mechanism into the neck network’s convolution, and replacing the detection head with the Decoupled_Detect head to establish the CD-YOLO network model. Finally, the algorithm is tested and compared on the developed intelligent cocoon selection system. The experimental results show that the grid-clustered cocoon quality detection algorithm based on CD-YOLO outperforms the original network model, with the mean average precision (mAP) increasing by 5.8%. The mAP for defects at 0.5 threshold reaches 0.975, and the detection speed improves by 60%, achieving 45 fps. The optimized algorithm achieves a cocoon classification accuracy of$\ge 97$% and meets the demand for a cocoon picking rate of$\ge 300$kg per hour. This significantly enhances the sorting efficiency and accuracy, providing technological support for the automation upgrade in the sericulture industry.
Source: IEEE Access
Authors: Jiajia Liu, Jiapeng Zhang, Zhongli Ma, Enhao Li, Jianying Yuan
Institution: Chengdu University of Information Technology
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,995
OpenAlex ID: W4405812577