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The Biological Asset Detection Model YOLOSC Based on Deep Learning
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Kun-lun GUAN, Si-wen ZHU, Yang-sen ZHANG*, Qi-hao CHENG, Xue-kai ZHANG
Science Technology and Engineering | 2025, 25(2) : 674 - 682
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Science Technology and Engineering | 2025, 25(2): 674-682
• Papers·Automation and Computational Technology •
The Biological Asset Detection Model YOLOSC Based on Deep Learning
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Kun-lun GUAN, Si-wen ZHU, Yang-sen ZHANG*, Qi-hao CHENG, Xue-kai ZHANG
Affiliations
  • Institute of Intelligent Information Processing, Beijing Information Science and Technology University, Beijing 100192, China
Published: 2025-01-18 doi: 10.12404/j.issn.1671-1815.2401029
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In order to improve the accuracy and efficiency of inventory counting in the process of monitoring and auditing biological assets, a biological asset detection model YOLOSC incorporating the attention mechanism and loss function optimization was proposed. Firstly, the SENet attention mechanism was introduced into the backbone network of the YOLOv5s model to enhance the ability of extracting the key features in the pictures of the biological assets. Secondly, the CIoU was adopted as the regression of the detection frames with the loss function to enhance the regression speed and localization accuracy of the detection frame during the training process. Finally, a biological asset datasets was constructed for targeted training of the proposed model to enhance the model detection effect. The experimental results show that compared with the YOLOv5model, the precision, recall, F1 value and AP of YOLOSC are improved by 2.3%, 2.1%, 2.7% and 1.6%, respectively, which proves the effectiveness of the proposed biological asset detection model YOLOSC.

target detection model  /  YOLOv5  /  attention mechanism  /  loss function  /  audit of biological assets
Kun-lun GUAN, Si-wen ZHU, Yang-sen ZHANG, Qi-hao CHENG, Xue-kai ZHANG. The Biological Asset Detection Model YOLOSC Based on Deep Learning[J]. Science Technology and Engineering, 2025 , 25 (2) : 674 -682 . DOI: 10.12404/j.issn.1671-1815.2401029
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Year 2025 volume 25 Issue 2
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Article Info
doi: 10.12404/j.issn.1671-1815.2401029
  • Receive Date：2024-02-07
  • Online Date：2025-12-05
  • Published：2025-01-18
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  • Received：2024-02-07
  • Revised：2024-10-23
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    Institute of Intelligent Information Processing, Beijing Information Science and Technology University, Beijing 100192, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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