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基于深度学习的机器手捡蛋中鹅蛋识别方法。

An approach for goose egg recognition for robot picking based on deep learning.

机构信息

College of Mechanical Engineering of Yangzhou University, Yangzhou, China.

出版信息

Br Poult Sci. 2023 Jun;64(3):343-356. doi: 10.1080/00071668.2023.2171769. Epub 2023 Apr 28.

DOI:10.1080/00071668.2023.2171769
PMID:36696133
Abstract
  1. In a non-cage environment, goose eggs are buried in litter and goose feathers, leading to contamination and discolouration. Such random distribution of goose eggs poses a great challenge to the recognition and location for intelligent picking by robot systems on farm.2. In order to assist in recognition and location of goose eggs in non-cage environments, a novel method was proposed which used three-channel convolutional neural network (T-CNN), composed of improved AlexNet, combined with 'you only look once' (YOLOv5), egg contour curve creation and support vector machine (SVM).3. Using this method, the original goose egg images were put into the YOLOv5 model for target detection and segmentation. In parallel, the median filter and maximum interclass variance method (OTSU) were applied to egg segmentation images to obtain the main pixels for each, and the Kirsch operator was used for edge extraction and contour curves fitting by designing the fitting curve equation to obtain segmentation images with goose egg contour curves.4. In order to further enrich the differences between goose eggs and background, the goose egg segmentation images were divided into three colour components: R, G and B, which were put into T-CNN for feature extraction. Then the eggs were classified by vector stitching and SVM, by adding goose egg contour curve images.5. The recognition and location results showed that about 95.65% of the goose egg pixel blocks in the segmented images were recognised correctly. About 3.81% of the pixel blocks in the segmented images were recognised incorrectly, and the centre of mass offset was about 4.45 pixels.6. This study demonstrated accurate goose egg recognition and location using the proposed method in a non-cage environment. This highlighted its application prospect in intelligent goose egg picking as well as a possible method to use in other species laying eggs outside the nest (floor eggs) as for example laying hens in non cage systems.
摘要
  1. 在非笼养环境中,鹅蛋被埋在垫料和鹅毛中,导致污染和变色。这种鹅蛋的随机分布对农场机器人系统的智能捡拾识别和定位提出了巨大挑战。

  2. 为了协助识别和定位非笼养环境中的鹅蛋,提出了一种新方法,该方法使用三通道卷积神经网络(T-CNN),由改进的 AlexNet 组成,结合“你只需看一次”(YOLOv5)、蛋轮廓曲线创建和支持向量机(SVM)。

  3. 使用这种方法,将原始鹅蛋图像输入 YOLOv5 模型进行目标检测和分割。同时,应用中值滤波器和最大类间方差法(OTSU)对蛋分割图像进行处理,以获得每个蛋的主要像素,并使用 Kirsch 算子进行边缘提取和轮廓曲线拟合,设计拟合曲线方程以获得具有鹅蛋壳轮廓曲线的分割图像。

  4. 为了进一步丰富鹅蛋与背景之间的差异,将鹅蛋分割图像分为 R、G 和 B 三个颜色分量,输入 T-CNN 进行特征提取。然后,通过添加鹅蛋壳轮廓曲线图像,通过矢量拼接和 SVM 对蛋进行分类。

  5. 识别和定位结果表明,分割图像中约 95.65%的鹅蛋像素块被正确识别。分割图像中约 3.81%的像素块被错误识别,质心偏移约 4.45 像素。

  6. 本研究在非笼养环境中使用提出的方法实现了对鹅蛋的准确识别和定位。这突显了其在智能鹅蛋采摘中的应用前景,以及在其他不在巢中产蛋的物种(如非笼养系统中的蛋鸡)中可能使用的方法。

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