FEDM:基于卷积神经网络的受精卵检测模型。

IF 1.6 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE British Poultry Science Pub Date : 2024-10-01 Epub Date: 2024-06-03 DOI:10.1080/00071668.2024.2356656
Z Gong, M Wang, J Song
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引用次数: 0

摘要

1.鹅蛋的生产在全球范围内具有重要的经济价值,而受精蛋的质量对于家禽业的成功孵化和持续发展至关重要。开发适合大规模检测的低成本受精蛋鉴定系统意义重大。然而,现有方法成本高、环境检测要求高,限制了其推广。 针对这一问题,提出了一种基于 YOLOv5 的改进对象检测模型 FEDM,该模型在九种模型中表现突出。在 YOLOv5 的主网络中加入了 SENet 注意机制,以提高特征选择能力。引入 C3_DCNv3 增强了对受精卵中血管的检测能力。Dyhead 的应用大大提高了物体检测头的表示能力,而且没有任何计算开销。3. 增强数据集的实验结果表明,FEDM 的平均精度达到 96.7%,比 YOLOv5s 模型提高了 5.5%。4. 与 YOLOv5 算法相比,FEDM 确保了第四天受精卵检测率的显著提高。基于这一结果,可以节省和利用空间,具有实际应用价值。
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FEDM: a convolutional neural network based fertilised egg detection model.

1. The production of goose eggs holds significant economic value on a global scale and the quality of fertilised eggs is crucial for the successful hatching and sustained development of the poultry industry. Developing a low-cost fertilised egg identification system that is suitable for large-scale testing is of great significance. However, existing methods are expensive and have high environmental detection requirements, which limit their promotion.2. To address this issue, an improved object detection model called FEDM based on YOLOv5 is proposed, which has been shown to be outstanding among nine models. The main network of YOLOv5 is enhanced with the SENet attention mechanism to improve the feature selection capability. The C3_DCNv3 is introduced to enhance the detection ability of blood vessels in the fertilised eggs. The application of Dyhead significantly improved the representation capacity of the object detection head without any computational overhead. The loss function is replaced with MPDIoU to simplify the calculation process.3. Experimental results from the augmented dataset showed that the average precision of the FEDM reached 96.7%, which is a 5.5% improvement compared to the YOLOv5s model. FEDM exhibited better detection performance on eggs from different shooting angles than the YOLOv5 algorithm and achieves high detection speed.4. The FEDM secured significant advancement on the detection rate of the fourth day fertilised egg compared to the YOLOv5 algorithm. Based on this result, savings and space utilisation can be made, which has practical application value.

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来源期刊
British Poultry Science
British Poultry Science 农林科学-奶制品与动物科学
CiteScore
3.90
自引率
5.00%
发文量
88
审稿时长
4.5 months
期刊介绍: From its first volume in 1960, British Poultry Science has been a leading international journal for poultry scientists and advisers to the poultry industry throughout the world. Over 60% of the independently refereed papers published originate outside the UK. Most typically they report the results of biological studies with an experimental approach which either make an original contribution to fundamental science or are of obvious application to the industry. Subjects which are covered include: anatomy, embryology, biochemistry, biophysics, physiology, reproduction and genetics, behaviour, microbiology, endocrinology, nutrition, environmental science, food science, feeding stuffs and feeding, management and housing welfare, breeding, hatching, poultry meat and egg yields and quality.Papers that adopt a modelling approach or describe the scientific background to new equipment or apparatus directly relevant to the industry are also published. The journal also features rapid publication of Short Communications. Summaries of papers presented at the Spring Meeting of the UK Branch of the WPSA are published in British Poultry Abstracts .
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