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<p>Robust and efficient detection of young “Yuluxiang” pears fruits has a significant challenge in natural environments. This difficulty arises from factors such as the similar color between young fruits and the background, occlusion from branches and leaves, fruit denseness, and small fruit size. To achieve the precise detection, a lightweight detection method named YOLO-CiHFC was proposed in this study. The CiR module was constructed using the Inverted Residual Mobile Block (iRMB) and C2f module. The C2f modules of the YOLOv8n backbone and neck networks were all replaced with CiR modules to maintain low computational complexity and the number of parameters, and to enhance the feature extraction and fusion capabilities of the model. Then, the HS-FPN structure was introduced to reconstruct the neck network, and the Focaler-CIoU was introduced as the loss function of models. In comparison with the YOLOv8n, the F1 score and average precision (AP) of the YOLO-CiHFC improved by 0.25% and 1.77%, respectively. The inference time (achieving 1.5&#xa0;ms) of the YOLO-CiHFC was 0.2&#xa0;ms faster than the YOLOv8n. The model size of YOLO-CiHFC was 52.94% of the original model. Furthermore, a comparison was performed between the YOLO-CiHFC model and common lightweight models, such as YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5n, and YOLOv7-Tiny. The results showed that the YOLO-CiHFC model achieved the optimal F1 score of 85.95% and AP of 88.00%, had the smallest model size of 3.15&#xa0;MB, and obtained the best detection results under different scenarios. The YOLO-CiHFC model was deployed in Jetson nano at a real-time detection speed of 25.5 f/s. In this study, the proposed YOLO-CiHFC method not only achieved lightweight, but also improved the accuracy and speed of detection. This study can provide the methodological support for intelligent detection of young “Yuluxiang” pears fruits.</p>

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Detection of young fruit for “Yuluxiang” pears in natural environments using YOLO-CiHFC

  • 期刊论文

  • 发表日期：

    2025年3月10日

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  • Haixia Sun,
  • Rui Ren,
  • Shujuan Zhang,
  • Sheng Yang,
  • Tianyu Cui,
  • Meng Su

摘要

Robust and efficient detection of young “Yuluxiang” pears fruits has a significant challenge in natural environments. This difficulty arises from factors such as the similar color between young fruits and the background, occlusion from branches and leaves, fruit denseness, and small fruit size. To achieve the precise detection, a lightweight detection method named YOLO-CiHFC was proposed in this study. The CiR module was constructed using the Inverted Residual Mobile Block (iRMB) and C2f module. The C2f modules of the YOLOv8n backbone and neck networks were all replaced with CiR modules to maintain low computational complexity and the number of parameters, and to enhance the feature extraction and fusion capabilities of the model. Then, the HS-FPN structure was introduced to reconstruct the neck network, and the Focaler-CIoU was introduced as the loss function of models. In comparison with the YOLOv8n, the F1 score and average precision (AP) of the YOLO-CiHFC improved by 0.25% and 1.77%, respectively. The inference time (achieving 1.5 ms) of the YOLO-CiHFC was 0.2 ms faster than the YOLOv8n. The model size of YOLO-CiHFC was 52.94% of the original model. Furthermore, a comparison was performed between the YOLO-CiHFC model and common lightweight models, such as YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5n, and YOLOv7-Tiny. The results showed that the YOLO-CiHFC model achieved the optimal F1 score of 85.95% and AP of 88.00%, had the smallest model size of 3.15 MB, and obtained the best detection results under different scenarios. The YOLO-CiHFC model was deployed in Jetson nano at a real-time detection speed of 25.5 f/s. In this study, the proposed YOLO-CiHFC method not only achieved lightweight, but also improved the accuracy and speed of detection. This study can provide the methodological support for intelligent detection of young “Yuluxiang” pears fruits.