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A Method for Cucumber Fruit Recognition Under Uniform-color Backgrounds
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Liying CAO, Yang LIU, Xilin WANG*, Hengyu ZHOU, Donghui JIANG
Radio Communications Technology | 2025, 51(5) : 1036 - 1045
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Radio Communications Technology | 2025, 51(5): 1036-1045
Special Topic:Frontiers in Intelligent Communication, Storage, and Information Processing Technologies
A Method for Cucumber Fruit Recognition Under Uniform-color Backgrounds
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Liying CAO, Yang LIU, Xilin WANG*, Hengyu ZHOU, Donghui JIANG
Affiliations
  • College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Published: 2025-09-18 doi: 10.3969/j.issn.1003-3114.2025.05.016
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Compared with a different-color backgrounds, recognizing and detecting cucumber fruits under uniform-color backgrounds remains a key challenge due to limited distinguishing features and increased susceptibility to occlusion and background interference. To address this, we propose YOLO-ACG, a detection network based on YOLOv11n. An Adaptive Dynamic Downsample (A-Down) module is introduced, combining deformable convolution and channel attention to achieve adaptive cross-scale feature sampling. A Ghost_HGNetV2 architecture is designed, where the High-resolution Group Stem (HGStem) reduces input channels to extract efficient intrinsic features, and the Ghost_HGBlock applies knowledge distillation to enhance feature representation. A Context and Spatial Feature Calibration Network (CSFCN) network structure is introduced, which includes Context Feature Calibration (CFC) and Spatial Feature Calibration (SFC). The CFC module aggregates context information relevant to each pixel, while the SFC module leverages calibrated spatial features to ensure accurate understanding of spatial layout the image. Together, they enable the network to more precisely distinguish cucumber fruits from backgrounds with similar colors. Experimental results show that the improved model achieves 4.64 percentage points increase in precision, recall by 5.07 percentage points, F1 by 4.89 percentage points, and mAP by 4.48 percentage points. Ablation and comparative experiments confirm that YOLO-ACG significantly reduces false positives and missed detections, offering effective technical support for cucumber fruits recognition in complex, uniform-color environments.

feature extraction  /  YOLO  /  fruit identification  /  object detection
Liying CAO, Yang LIU, Xilin WANG, Hengyu ZHOU, Donghui JIANG. A Method for Cucumber Fruit Recognition Under Uniform-color Backgrounds[J]. Radio Communications Technology, 2025 , 51 (5) : 1036 -1045 . DOI: 10.3969/j.issn.1003-3114.2025.05.016
Year 2025 volume 51 Issue 5
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doi: 10.3969/j.issn.1003-3114.2025.05.016
  • Receive Date:2025-06-06
  • Online Date:2026-04-17
  • Published:2025-09-18
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  • Received:2025-06-06
Affiliations
    College of Information Technology, Jilin Agricultural University, Changchun 130118, 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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