Real-time detection of overturned meat ducks based on YOLOv8-Swin Transformer model

doi:10.3969/j.issn.1004-1524.20240333

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (7): 1556-1566.DOI: 10.3969/j.issn.1004-1524.20240333

• Biosystems Engineering • Previous Articles Next Articles

Real-time detection of overturned meat ducks based on YOLOv8-Swin Transformer model

LYU Yinchun¹^,²(), DUAN Enze², ZHU Yixing², ZHENG Xia², BAI Zongchun²^,³^,^*()

1. College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, Jiangsu, China
2. Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
3. Key Laboratory of Protected Agriculture Engineering in the Middle and Lower Reaches of Yangtze River, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

Received:2024-08-05 Online:2025-07-25 Published:2025-08-20

Abstract

Abstract:

To address the challenges of detecting small and easily occluded meat ducks in cages within large-scale farms, as well as the difficulty of deploying existing detection methods for overturned ducks on embedded devices, this study proposes a detection method for identifying overturned meat ducks suitable for deployment on Jetson Orin. This approach ensures accurate detection of overturned ducks while achieving lightweight model deployment and improved detection efficiency. A dataset of 1 000 images of overturned meat ducks was constructed and divided into training, testing, and validation sets in an 8∶1∶1 ratio. A deep learning network was employed to extract behavioral features of overturned ducks and build a target detection model. The Swin Transformer-tiny module was integrated to replace the backbone network of YOLOv8, significantly enhancing the detection capability for small targets in complex environments. Model pruning and quantization were applied to reduce computational complexity while maintaining accuracy, achieving a better balance between model precision and speed. When deploying the optimized models on embedded devices with a confidence threshold set to 60, the YOLOv8n-Swin Transformer and YOLOv8s-Swin Transformer models demonstrated average recognition accuracies of 96.0% and 97.1%, respectively, for detecting overturned meat ducks. Their false recognition rates were 2.7% and 2.0%, while the single-frame image processing times measured 6.8 ms and 7.4 ms, respectively.

Key words: machine vision, overturned meat duck recognition, caged meat duck, deep learning, facility breeding

CLC Number:

LYU Yinchun, DUAN Enze, ZHU Yixing, ZHENG Xia, BAI Zongchun. Real-time detection of overturned meat ducks based on YOLOv8-Swin Transformer model[J]. Acta Agriculturae Zhejiangensis, 2025, 37(7): 1556-1566.

Figures/Tables 9

Fig.1 Inspection equipment size and relative position between camera and duck cage

Fig.2 Image enhancement process

Fig.3 Architecture of YOLOv8 network C, Convolutional layer; U, Upsample; Bbox, Bounding box; Cls, Classification score; C2F, Cross stage partial bottleneck with two convolutions; Conv, Convolutional layer. The same as below.

Fig.4 Overall architecture of Swin Transformer block LN, Layer normalization; W-MSA, Window-based multi-head self-attention; SW-MSA, Shifted window multi-head self-attention; MLP, Muti-layer perceptron.

Fig.5 Flow of window moving action a, Input image; b, Window segmentation via W-MSA; c, Moving window effect; d, Window segmentation via SW-MSA.

Fig.6 Improved YOLOv8 network structure based on Swin Transformer

Fig.7 Curves of bounding box loss and classification loss

Fig.8 Curves of precision, recall and mean average precision

Table 1 Performance of different models on meat duck turnover recognition under different confidence thresholds

模型 Model	置信度阈值 Confidence threshold	误检数 False detection	漏检数 Missed detection	识别平均准确率 Average recognition accuracy/%	识别误检率 False recognition rate/%	单帧图像处理时间 Single frame image processing time/ms
YOLOv5n	60	6	1	93.4	4.9	4.8
	90	3	9	88.3	8.7	4.8
YOLOv8n	60	5	1	94.3	4.2	4.4
	90	3	8	89.3	7.9	4.4
YOLOv8n-Swin Transformer	60	4	0	96.0	2.7	6.8
	90	1	7	92.1	5.6	6.8
YOLOv8s-Swin Transformer	60	3	0	97.1	2.0	7.4
	90	1	6	93.1	4.9	7.4

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Real-time detection of overturned meat ducks based on YOLOv8-Swin Transformer model

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