Background Nowadays, robots have been widely used in handling rigid objects, but research on deformable objects like fabrics is still in its early stages. This is because fabrics possess infinite degrees of freedom and their state modeling is highly complex, making robot manipulation of fabrics challenging due to the occurrence of wrinkles and deformations during the operation. The detection and recognition of fabric deformations such as wrinkles and fabric manipulation features like corners are of great significance in enhancing a robot's capability to handle deformable objects. Methods In response to the issue of fabric wrinkles in various scenarios, we propose a real-time fabric wrinkle and corner detection system based on the YOLOv5 detection algorithm. Additionally, we implement a fabric flattening operation on a hardware platform using the detected wrinkle and corner information. Results We collected and created a dataset of fabric deformation features and trained a detection model, achieving a detection accuracy of over 90%. The model was deployed in the fabric wrinkle detection system, using a heuristic operation strategy of flattening the fabric from the four corners. As a result, the robot successfully performed the flattening operation on wrinkled fabric. Conclusions The application of the YOLOv5 algorithm enables effective detection of fabric wrinkles and corner points. Based on the detection information and using the quadrilateral flattening operation method, the robotic system achieves fabric flattening operations.
{"title":"Design and Implementation of Fabric Wrinkle Detection System Based on YOLOv5 Algorithm","authors":"Cheng Li, Tianyu Fu, Fengming Li, Rui Song","doi":"10.12688/cobot.17687.1","DOIUrl":"https://doi.org/10.12688/cobot.17687.1","url":null,"abstract":"Background Nowadays, robots have been widely used in handling rigid objects, but research on deformable objects like fabrics is still in its early stages. This is because fabrics possess infinite degrees of freedom and their state modeling is highly complex, making robot manipulation of fabrics challenging due to the occurrence of wrinkles and deformations during the operation. The detection and recognition of fabric deformations such as wrinkles and fabric manipulation features like corners are of great significance in enhancing a robot's capability to handle deformable objects. Methods In response to the issue of fabric wrinkles in various scenarios, we propose a real-time fabric wrinkle and corner detection system based on the YOLOv5 detection algorithm. Additionally, we implement a fabric flattening operation on a hardware platform using the detected wrinkle and corner information. Results We collected and created a dataset of fabric deformation features and trained a detection model, achieving a detection accuracy of over 90%. The model was deployed in the fabric wrinkle detection system, using a heuristic operation strategy of flattening the fabric from the four corners. As a result, the robot successfully performed the flattening operation on wrinkled fabric. Conclusions The application of the YOLOv5 algorithm enables effective detection of fabric wrinkles and corner points. Based on the detection information and using the quadrilateral flattening operation method, the robotic system achieves fabric flattening operations.","PeriodicalId":505492,"journal":{"name":"Cobot","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background Auxiliary assembly refers to guiding and prompting the assembly process to help operators complete complex assembly operations. Due to the complex structure of products, the similar shape of parts and human factors, the misassembly and missing assembly of parts still occur in the process of product assembly, so it is of great significance to detect the assembly correctness of complex products. Methods Aiming at the problem that manual inspection is inefficient and depends heavily on the level of inspectors in the process of complex product assembly inspection, this paper proposes an assembly correctness detection method based on deep learning. Through the three steps of view transformation, semantic segmentation and template matching, the automatic judgment of assembly errors such as wrong assembly, missing assembly and redundancy is realized, and the method is verified by the computer motherboard. Results Taking the computer motherboard as the verification object to test the correctness of assembly, the experimental re sults show that the perspective adjustment of the image after homography transformation is very obvious. The evaluation index of the semantic segmentation network detection object is calculated, and each accuracy meets the requirements of assembly correctness detection. A visualization module is also used to visually display the results of assembly correctness detection based on template matching. Conclusions The assembly correctness detection method can provide a guarantee for the manual assembly process and reduce the error rate of assembly. The machine vision detection technology can be used for automatic detection of assembly quality to improve the efficiency and automation level of detection.
{"title":"Research on intelligent auxiliary assembly technology based on deep learning","authors":"Wang Yan, Wei Wei, Baitao Tang","doi":"10.12688/cobot.17668.1","DOIUrl":"https://doi.org/10.12688/cobot.17668.1","url":null,"abstract":"Background Auxiliary assembly refers to guiding and prompting the assembly process to help operators complete complex assembly operations. Due to the complex structure of products, the similar shape of parts and human factors, the misassembly and missing assembly of parts still occur in the process of product assembly, so it is of great significance to detect the assembly correctness of complex products. Methods Aiming at the problem that manual inspection is inefficient and depends heavily on the level of inspectors in the process of complex product assembly inspection, this paper proposes an assembly correctness detection method based on deep learning. Through the three steps of view transformation, semantic segmentation and template matching, the automatic judgment of assembly errors such as wrong assembly, missing assembly and redundancy is realized, and the method is verified by the computer motherboard. Results Taking the computer motherboard as the verification object to test the correctness of assembly, the experimental re sults show that the perspective adjustment of the image after homography transformation is very obvious. The evaluation index of the semantic segmentation network detection object is calculated, and each accuracy meets the requirements of assembly correctness detection. A visualization module is also used to visually display the results of assembly correctness detection based on template matching. Conclusions The assembly correctness detection method can provide a guarantee for the manual assembly process and reduce the error rate of assembly. The machine vision detection technology can be used for automatic detection of assembly quality to improve the efficiency and automation level of detection.","PeriodicalId":505492,"journal":{"name":"Cobot","volume":"8 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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