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Lightweight SCD-YOLOv5s: The Detection of Small Defects on Passion Fruit with Improved YOLOv5s

Author

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  • Yu Zhou

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Zhenye Li

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Sheng Xue

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Min Wu

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Tingting Zhu

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Chao Ni

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

Accurate detection of surface defects on passion fruits is crucial for maintaining market competitiveness. Numerous small defects present significant challenges for manual inspection. Recently, deep learning (DL) has been widely applied to object detection. In this study, a lightweight neural network, StarC3SE-CBAM-DIoU-YOLOv5s (SCD-YOLOv5s), is proposed based on YOLOv5s for real-time detection of tiny surface defects on passion fruits. Key improvements are introduced as follows: the original C3 module in the backbone is replaced by the enhanced StarC3SE module to achieve a more efficient network structure; the CBAM module is integrated into the neck to improve the extraction of small defect features; and the CIoU loss function is substituted with DIoU-NMS to accelerate convergence and enhance detection accuracy. Experimental results show that SCD-YOLOv5s performs better than YOLOv5s, with precision increased by 13.2%, recall by 1.6%, and F 1 - score by 17.0%. Additionally, improvements of 6.7% in mAP@0.5 and 5.5% in mAP@0.95 are observed. Compared with manual detection, the proposed model enhances detection efficiency by reducing errors caused by subjective judgment. It also achieves faster inference speed (26.66 FPS), and reductions of 9.6% in parameters and 8.6% in weight size, while maintaining high detection performance. These results indicate that SCD-YOLOv5s is effective for defect detection in agricultural applications.

Suggested Citation

  • Yu Zhou & Zhenye Li & Sheng Xue & Min Wu & Tingting Zhu & Chao Ni, 2025. "Lightweight SCD-YOLOv5s: The Detection of Small Defects on Passion Fruit with Improved YOLOv5s," Agriculture, MDPI, vol. 15(10), pages 1-26, May.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:10:p:1111-:d:1661216
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    References listed on IDEAS

    as
    1. Yajie He & Ningyi Zhang & Xinjin Ge & Siqi Li & Linfeng Yang & Minghao Kong & Yiping Guo & Chunli Lv, 2025. "Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing," Agriculture, MDPI, vol. 15(7), pages 1-24, March.
    2. Anne Pinheiro Costa & José Ricardo Peixoto & Luiz Eduardo Bassay Blum & Márcio de Carvalho Pires, 2019. "Standard Area Diagram Set for Scab Evaluation in Fruits of sour Passion Fruit," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 11(14), pages 298-298, September.
    3. Peng Wang & Tong Niu & Dongjian He, 2021. "Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism," Agriculture, MDPI, vol. 11(11), pages 1-13, October.
    4. Efrem Yohannes Obsie & Hongchun Qu & Yong-Jiang Zhang & Seanna Annis & Francis Drummond, 2022. "Yolov5s-CA: An Improved Yolov5 Based on the Attention Mechanism for Mummy Berry Disease Detection," Agriculture, MDPI, vol. 13(1), pages 1-23, December.
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