In British poultry science
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 of intelligent picking by robot systems on farm.2. In order to assist in recognition and location of goose eggs in non-cage environment, 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 inputted 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 input into T-CNN for feature extraction. Then the eggs were classified by vector stitching and SVM, by adding goose egg contour curves images.5. The recognition and location results showed that about 95.65% of the goose egg pixel blocks in the segmented images are 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 effects using the proposed method in a non-cage environment This highlighted its application prospect in intelligent goose eggs picking.
Zhang Yanjun, Ge Yujie, Guo Yangyang, Miao Hong, Zhang Shanwen
2023-Jan-25
Intelligent picking, YOLOv5, contour curve creation, deep learning, egg recognition and location
