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A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model

Author

Listed:
  • Mingjie Wu

    (School of Information, Yunnan Normal University, Kunming 650500, China
    Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, Kunming 650500, China)

  • Xuanxi Yang

    (Centre for Planning and Policy Research, Yunnan Institute of Forest Inventory and Planning, Kunming 650500, China)

  • Lijun Yun

    (School of Information, Yunnan Normal University, Kunming 650500, China
    Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, Kunming 650500, China)

  • Chenggui Yang

    (School of Information, Yunnan Normal University, Kunming 650500, China
    Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, Kunming 650500, China)

  • Zaiqing Chen

    (School of Information, Yunnan Normal University, Kunming 650500, China
    Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, Kunming 650500, China)

  • Yuelong Xia

    (School of Information, Yunnan Normal University, Kunming 650500, China
    Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, Kunming 650500, China)

Abstract

Object detection models are commonly used in yield estimation processes in intelligent walnut production. The accuracy of these models in capturing walnut features largely depends on the quality of the input images. Without changing the existing image acquisition devices, this study proposes a super-resolution reconstruction module for drone-acquired walnut images, named Walnut-SR, to enhance the detailed features of walnut fruits in images, thereby improving the detection accuracy of the object detection model. In Walnut-SR, a deep feature extraction backbone network called MDAARB (multilevel depth adaptive attention residual block) is designed to capture multiscale information through multilevel channel connections. Additionally, Walnut-SR incorporates an RRDB (residual-in-residual dense block) branch, enabling the module to focus on important feature information and reconstruct images with rich details. Finally, the CBAM (convolutional block attention module) attention mechanism is integrated into the shallow feature extraction residual branch to mitigate noise in shallow features. In 2× and 4× reconstruction experiments, objective evaluation results show that the PSNR and SSIM for 2× and 4× reconstruction reached 24.66 dB and 0.8031, and 19.26 dB and 0.4991, respectively. Subjective evaluation results indicate that Walnut-SR can reconstruct images with richer detail information and clearer texture features. Comparative experimental results of the integrated Walnut-SR module show significant improvements in mAP50 and mAP50:95 for object detection models compared to detection results using the original low-resolution images.

Suggested Citation

  • Mingjie Wu & Xuanxi Yang & Lijun Yun & Chenggui Yang & Zaiqing Chen & Yuelong Xia, 2024. "A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model," Agriculture, MDPI, vol. 14(8), pages 1-24, August.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1279-:d:1448908
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    References listed on IDEAS

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    1. Junmin Jia & Fei Hu & Xubo Zhang & Zongyou Ben & Yifan Wang & Kunjie Chen, 2023. "Method of Attention-Based CNN for Weighing Pleurotus eryngii," Agriculture, MDPI, vol. 13(9), pages 1-14, August.
    2. Guangyu Hou & Haihua Chen & Mingkun Jiang & Runxin Niu, 2023. "An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots," Agriculture, MDPI, vol. 13(9), pages 1-31, September.
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