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Improved YOLOv8 Bird Recognition Algorithm Integrates ViT and Multi-scale Attention
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Qiang ZHANG, Can-zhi ZHANG, Heng CAO, Teng-jiao YUAN
Science Technology and Engineering | 2025, 25(19) : 8151 - 8157
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Science Technology and Engineering | 2025, 25(19): 8151-8157
• Papers∙Automation and Computational Technology •
Improved YOLOv8 Bird Recognition Algorithm Integrates ViT and Multi-scale Attention
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Qiang ZHANG, Can-zhi ZHANG, Heng CAO, Teng-jiao YUAN
Affiliations
  • College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618300, China
Published: 2025-07-08 doi: 10.12404/j.issn.1671-1815.2405983
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In order to solve the problems of inaccurate dense target recognition and difficult detection of small targets in bird recognition, a bird recognition algorithm based on improved YOLOv8 was proposed. Firstly, in order to solve the problem of difficult dense object recognition, the multi-scale linear attention mechanism EfficientViT was used to replace the backbone network to realize the global receptive field and multi-scale learning, improve the performance and efficiency of the model, and improve the dense object recognition effect. Then, in order to solve the problem that it is difficult to detect small target birds and is prone to missed detection, an efficient multi-scale attention EMA (efficient multi-scale attention) mechanism was introduced to realize cross-dimensional aggregation features through channel recombination, so as to better capture global information, realize multi-scale feature fusion, and reduce the probability of missed detection. The experimental results show that the mAP50 of the improved model on the benchmark dataset CUB-200-2011 and birds28 reaches 77.1% and 88.4%, respectively, which is 4.5 and 5.4 percentage points higher than the original YOLOv8 model, respectively, which verifies the effectiveness of the improved model.

bird recognition  /  multi-scale attention  /  dense target recognition  /  YOLOv8  /  EfficientViT  /  EMA
Qiang ZHANG, Can-zhi ZHANG, Heng CAO, Teng-jiao YUAN. Improved YOLOv8 Bird Recognition Algorithm Integrates ViT and Multi-scale Attention[J]. Science Technology and Engineering, 2025 , 25 (19) : 8151 -8157 . DOI: 10.12404/j.issn.1671-1815.2405983
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Year 2025 volume 25 Issue 19
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doi: 10.12404/j.issn.1671-1815.2405983
  • Receive Date：2024-08-09
  • Online Date：2025-12-22
  • Published：2025-07-08
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  • Received：2024-08-09
  • Revised：2024-12-23
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    College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618300, 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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