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

人工智能 模式识别(心理学) 像素 分割 支持向量机 计算机科学 计算机视觉 生物 生态学
作者
Yanhua Zhang,Yonggang Ge,Yuxing Guo,Hong Miao,Shanwen Zhang
出处
期刊:British Poultry Science [Taylor & Francis]
卷期号:64 (3): 343-356 被引量:6
标识
DOI:10.1080/00071668.2023.2171769
摘要

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.
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