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High-altitude Nut Recognition Algorithm Based on Improved YOLOv5
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Fang-fang MENG1, Xiao-zhuang TIAN1, Wei FANG2, *, Dong-ying ZHANG2, Yun ZAN1, Chen ZHANG1, Peng ZHAN1
Science Technology and Engineering | 2025, 25(1) : 262 - 269
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Science Technology and Engineering | 2025, 25(1): 262-269
Papers·Automation and Computational Technology
High-altitude Nut Recognition Algorithm Based on Improved YOLOv5
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Fang-fang MENG1, Xiao-zhuang TIAN1, Wei FANG2, *, Dong-ying ZHANG2, Yun ZAN1, Chen ZHANG1, Peng ZHAN1
Affiliations
  • 1. School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
  • 2. Institute of Intelligent Machinery, Hefei Institute of Material Science, Chinese Academy of Sciences, Hefei 230031, China
Published: 2025-01-08 doi: 10.12404/j.issn.1671-1815.2402469
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In order to improve the recognition accuracy of high-altitude nuts and reduce the false detection rate of bolts and nuts, a high-altitude nut recognition model based on improved YOLOv5 was proposed. Firstly, a new attention mechanism efficient multi-scale attention(EMA) was added to the backbone network to integrate more information. Secondly, in order to enhance the network’s feature extraction capability, bidirectional feature pyramid network(BiFPN) was used to replace the PANet of the neck network. Finally, structured intersection over union(SIoU) was used to replace the original loss function complete intersection-over-union(CIoU) to accelerate the convergence of the model and improve its classification accuracy. The results show that the improved model has better performance than the original YOLOv5 model. The accuracy of the improved model increases by 0.92%. The recall increases by 0.16%. The average precision 1 (mAP_0.5:0.5) increases by 0.53%. And the average precision 2 (mAP:0.95) increases by 2.26%. An actual recognition comparison experiment between the improved model and the original YOLOv5 model was carried out. The experimental results show that the improved model has better recognition performance, which reduces the missed detection rate and the false detection rate, and improves the actual recognition rate. The improved model can well meet the recognition and image data acquisition of high-altitude nuts. And it also provide a data foundation for subsequent nut maintenance.

nut  /  object detection  /  YOLOv5  /  attention mechanism
Fang-fang MENG, Xiao-zhuang TIAN, Wei FANG, Dong-ying ZHANG, Yun ZAN, Chen ZHANG, Peng ZHAN. High-altitude Nut Recognition Algorithm Based on Improved YOLOv5[J]. Science Technology and Engineering, 2025 , 25 (1) : 262 -269 . DOI: 10.12404/j.issn.1671-1815.2402469
Year 2025 volume 25 Issue 1
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Article Info
doi: 10.12404/j.issn.1671-1815.2402469
  • Receive Date:2024-04-07
  • Online Date:2025-07-29
  • Published:2025-01-08
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  • Received:2024-04-07
  • Revised:2024-10-15
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Affiliations
    1. School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
    2. Institute of Intelligent Machinery, Hefei Institute of Material Science, Chinese Academy of Sciences, Hefei 230031, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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