Abstract: A surface defect detection method applied to a surface of an object is disclosed. The method includes obtaining an image of the surface, performing a deep learning algorithm by a computing device to set a plurality of bounding boxes in the image and to output a plurality of feature parameter sets associated with the plurality of bounding boxes, with each bounding box enclosing a possible defect of the surface, and performing a classifying algorithm by the computing device according to the bounding boxes and the feature parameters to determine whether the surface conforms to a specification.

Abstract: A surface defect detection method applied to a surface of an object is disclosed. The method includes obtaining an image of the surface, performing a deep learning algorithm by a computing device to set a plurality of bounding boxes in the image and to output a plurality of feature parameter sets associated with the plurality of bounding boxes, with each bounding box enclosing a possible defect of the surface, and performing a classifying algorithm by the computing device according to the bounding boxes and the feature parameters to determine whether the surface conforms to a specification.

Abstract: A framework of cloud service for biological growth is disclosed. The framework of cloud service for biological growth includes a client end, a service provider, an expert end, and an equipment end. The client end has a biological cultivate area, a first processing unit, a first control module, and a first transceiver module. The service provider has a first database, a second processing unit, a data integration module, a web server, a data integration module, and a second transceiver module. The expert end has a second database, a third processing unit, a second monitoring module, a biological growth simulation module, and a third transceiver module. The equipment end has a third database, a fourth processing unit, and a fourth transceiver module.

Abstract: A framework of cloud service for biological growth is disclosed. The framework of cloud service for biological growth includes a client end, a service provider, an expert end, and an equipment end. The client end has a biological cultivate area, a first processing unit, a first control module, and a first transceiver module. The service provider has a first database, a second processing unit, a data integration module, a web server, a data integration module, and a second transceiver module. The expert end has a second database, a third processing unit, a second monitoring module, a biological growth simulation module, and a third transceiver module. The equipment end has a third database, a fourth processing unit, and a fourth transceiver module.