Research on high-speed classification and location algorithm for logistics parcels based on a monocular camera
Abstract The rapid development of the logistics industry has driven innovations in parcel sorting technology, among which the swift and precise positioning and classification of parcels have become key to enhancing the performance of logistics systems. This study aims to address the limitations of t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-66941-x |
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| author | Zhehao Lu Ning Dai Xudong Hu Kaixin Xu Yanhong Yuan |
| author_facet | Zhehao Lu Ning Dai Xudong Hu Kaixin Xu Yanhong Yuan |
| author_sort | Zhehao Lu |
| collection | DOAJ |
| description | Abstract The rapid development of the logistics industry has driven innovations in parcel sorting technology, among which the swift and precise positioning and classification of parcels have become key to enhancing the performance of logistics systems. This study aims to address the limitations of traditional light curtain positioning methods in logistics sorting workshops amidst high-speed upgrades. This paper proposes a high-speed classification and location algorithm for logistics parcels utilizing a monocular camera. The algorithm combines traditional visual processing methods with an enhanced version of the lightweight YOLOv5 object detection algorithm, achieving high-speed, high-precision parcel positioning. Through the adjustment of the network structure and the incorporation of new feature extraction modules and ECIOU loss functions, the model’s robustness and detection accuracy have been significantly improved. Experimental results demonstrate that this model exhibits outstanding performance on a customized logistics parcel dataset, notably enhancing the model's parameter efficiency and computational speed, thereby offering an effective solution for industrial applications in high-speed logistics supply. |
| format | Article |
| id | doaj-art-410b7e2cef2a4b708a60b87093822cda |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-410b7e2cef2a4b708a60b87093822cda2025-02-02T12:25:26ZengNature PortfolioScientific Reports2045-23222024-07-0114111610.1038/s41598-024-66941-xResearch on high-speed classification and location algorithm for logistics parcels based on a monocular cameraZhehao Lu0Ning Dai1Xudong Hu2Kaixin Xu3Yanhong Yuan4Key Laboratory of Modern Textile Machinery&Technology of Zhejiang Province, Zhejiang Sci-Tech UniversityKey Laboratory of Modern Textile Machinery&Technology of Zhejiang Province, Zhejiang Sci-Tech UniversityKey Laboratory of Modern Textile Machinery&Technology of Zhejiang Province, Zhejiang Sci-Tech UniversityKey Laboratory of Modern Textile Machinery&Technology of Zhejiang Province, Zhejiang Sci-Tech UniversityKey Laboratory of Modern Textile Machinery&Technology of Zhejiang Province, Zhejiang Sci-Tech UniversityAbstract The rapid development of the logistics industry has driven innovations in parcel sorting technology, among which the swift and precise positioning and classification of parcels have become key to enhancing the performance of logistics systems. This study aims to address the limitations of traditional light curtain positioning methods in logistics sorting workshops amidst high-speed upgrades. This paper proposes a high-speed classification and location algorithm for logistics parcels utilizing a monocular camera. The algorithm combines traditional visual processing methods with an enhanced version of the lightweight YOLOv5 object detection algorithm, achieving high-speed, high-precision parcel positioning. Through the adjustment of the network structure and the incorporation of new feature extraction modules and ECIOU loss functions, the model’s robustness and detection accuracy have been significantly improved. Experimental results demonstrate that this model exhibits outstanding performance on a customized logistics parcel dataset, notably enhancing the model's parameter efficiency and computational speed, thereby offering an effective solution for industrial applications in high-speed logistics supply.https://doi.org/10.1038/s41598-024-66941-xLogistics parcel positioningMonocular camera algorithmYOLOv5High-speed logistics application |
| spellingShingle | Zhehao Lu Ning Dai Xudong Hu Kaixin Xu Yanhong Yuan Research on high-speed classification and location algorithm for logistics parcels based on a monocular camera Scientific Reports Logistics parcel positioning Monocular camera algorithm YOLOv5 High-speed logistics application |
| title | Research on high-speed classification and location algorithm for logistics parcels based on a monocular camera |
| title_full | Research on high-speed classification and location algorithm for logistics parcels based on a monocular camera |
| title_fullStr | Research on high-speed classification and location algorithm for logistics parcels based on a monocular camera |
| title_full_unstemmed | Research on high-speed classification and location algorithm for logistics parcels based on a monocular camera |
| title_short | Research on high-speed classification and location algorithm for logistics parcels based on a monocular camera |
| title_sort | research on high speed classification and location algorithm for logistics parcels based on a monocular camera |
| topic | Logistics parcel positioning Monocular camera algorithm YOLOv5 High-speed logistics application |
| url | https://doi.org/10.1038/s41598-024-66941-x |
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