YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union

Hao, Wangli; Zhang, Li; Han, Meng; Zhang, Kai; Li, Fuzhong; Yang, Guoqiang; Liu, Zhenyu

doi:10.3390/ani13203201

Cited by 2 publications

(1 citation statement)

References 37 publications

(40 reference statements)

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“…Yang et al [23] proposed an improved YOLOv5n pig inventory algorithm based on the construction of a multi-scene pig dataset and the introduction of the SE channel attention module, which improved the accuracy and robustness of the algorithm in complex occluded overlapping scenarios, and the algorithm's MAE was 0.173. Hao et al [24] proposed a novel pig detection and counting model based on YOLOv5, which combines the shuffle attention and Focal-CIoU loss; the shuffle attention module realizes multi-channel information fusion and enhances feature extraction performance, and the improved model achieves a high mAP and accuracy of 93.8% and 95.6% in pig detection and counting, respectively.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT

Shao,

Liu,

Zhou

et al. 2024

Animals

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A pig inventory is a crucial component of achieving precise and large-scale farming. In complex pigsty environments, due to pigs’ stress reactions and frequent obstructions, it is challenging to count them accurately and automatically. This difficulty contrasts with most current deep learning studies, which rely on overhead views or static images for counting. This research proposes a video-based dynamic counting method, combining YOLOv7 with DeepSORT. By utilizing the YOLOv7 network structure and optimizing the second and third 3 × 3 convolution operations in the head network ELAN-W with PConv, the model reduces the computational demand and improves the inference speed without sacrificing accuracy. To ensure that the network acquires accurate position perception information at oblique angles and extracts rich semantic information, we introduce the coordinate attention (CA) mechanism before the three re-referentialization paths (REPConv) in the head network, enhancing robustness in complex scenarios. Experimental results show that, compared to the original model, the improved model increases the mAP by 3.24, 0.05, and 1.00 percentage points for oblique, overhead, and all pig counting datasets, respectively, while reducing the computational cost by 3.6 GFLOPS. The enhanced YOLOv7 outperforms YOLOv5, YOLOv4, YOLOv3, Faster RCNN, and SSD in target detection with mAP improvements of 2.07, 5.20, 2.16, 7.05, and 19.73 percentage points, respectively. In dynamic counting experiments, the improved YOLOv7 combined with DeepSORT was tested on videos with total pig counts of 144, 201, 285, and 295, yielding errors of -3, -3, -4, and -26, respectively, with an average accuracy of 96.58% and an FPS of 22. This demonstrates the model’s capability of performing the real-time counting of pigs in various scenes, providing valuable data and references for automated pig counting research.

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Section: Introductionmentioning

confidence: 99%

Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT

Shao,

Liu,

Zhou

et al. 2024

Animals

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Accurate Non-Destructive Testing Method for Potato Sprouts Focusing on Deformable Attention

GENG,

DAI,

ZHANG

et al. 2024

INMATEH

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Accurate potato sprout detection is the key to automatic seed potato cutting, which is important for potato quality and yield. In this paper, a lightweight DAS-YOLOv8 model is proposed for the potato sprout detection task. By embedding DAS deformable attention in the feature extraction network and the feature fusion network, the global feature context can be efficiently represented and the attention increased to the relevant pixel image region; then, the C2f_Atten module fusing Shuffle attention is designed based on the C2f module to satisfy the attention to the key feature information of the high-level abstract semantics of the feature extraction network. At the same time, the ghost convolution is introduced to improve the C2f module and convolutional module to realize the decomposition of the redundant features to extract the key features. Verified on the collected potato sprout image data set, the average accuracy of the proposed DAS-YOLOv8 model is 94.25%, and the calculation amount is only 7.66 G. Compared with the YOLOv8n model, the accuracy is 2.13% higher, and the average accuracy is 1.55% higher. In comparison to advanced state-of-the-art (SOTA) target detection algorithms, the method in this paper offers a better balance between comprehensive performance and lightweight model design. The improved and optimized DAS-YOLOv8 model can realize the effective detection of potato sprouts, meet the requirements of real-time processing, and can provide theoretical support for the non-destructive detection of sprouts in automatic seed potato cutting.

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YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union

Cited by 2 publications

References 37 publications

Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT

Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT

Accurate Non-Destructive Testing Method for Potato Sprouts Focusing on Deformable Attention

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