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The Detection of Ear Tag Dropout in Breeding Pigs Using a Fused Attention Mechanism in a Complex Environment

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Listed:
  • Fang Wang

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohot 010018, China)

  • Xueliang Fu

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohot 010018, China)

  • Weijun Duan

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohot 010018, China)

  • Buyu Wang

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohot 010018, China)

  • Honghui Li

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohot 010018, China)

Abstract

The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic breeding data. Therefore, the identification of ear tag dropout is crucial for intelligent breeding in pig farms. In the production environment, promptly detecting breeding pigs with missing ear tags is challenging due to clustering overlap, small tag targets, and uneven sample distributions. This study proposes a method for detecting the dropout of breeding pigs’ ear tags in a complex environment by integrating an attention mechanism. Firstly, the approach involves designing a lightweight feature extraction module called IRDSC using depthwise separable convolution and an inverted residual structure; secondly, the SENet channel attention mechanism is integrated for enhancing deep semantic features; and finally, the IRDSC and SENet modules are incorporated into the backbone network of Cascade Mask R-CNN and the loss function is optimized with Focal Loss. The proposed algorithm, Cascade-TagLossDetector, achieves an accuracy of 90.02% in detecting ear tag dropout in breeding pigs, with a detection speed of 25.33 frames per second (fps), representing a 2.95% improvement in accuracy, and a 3.69 fps increase in speed compared to the previous method. The model size is reduced to 443.03 MB, a decrease of 72.90 MB, which enables real-time and accurate dropout detection while minimizing the storage requirements and providing technical support for the intelligent breeding of pigs.

Suggested Citation

  • Fang Wang & Xueliang Fu & Weijun Duan & Buyu Wang & Honghui Li, 2024. "The Detection of Ear Tag Dropout in Breeding Pigs Using a Fused Attention Mechanism in a Complex Environment," Agriculture, MDPI, vol. 14(4), pages 1-15, March.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:4:p:530-:d:1365122
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    References listed on IDEAS

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    1. Fang Wang & Xueliang Fu & Weijun Duan & Buyu Wang & Honghui Li, 2023. "Visual Detection of Lost Ear Tags in Breeding Pigs in a Production Environment Using the Enhanced Cascade Mask R-CNN," Agriculture, MDPI, vol. 13(10), pages 1-15, October.
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