Abstract: A use of a ZmSBP12 gene in the regulation of drought resistance, plant height, and ear height of Zea mays L. is provided. After the ZmSBP12 gene is over-expressed in Zea mays L., the resulting Zea mays L. mutant plant exhibits increased drought resistance and decreased plant and ear heights. The overexpression of the ZmSBP12 gene leads to increased drought resistance and decreased plant and ear heights, indicating that the ZmSBP12 gene plays a crucial role in the drought resistance and plant type (plant height) of Zea mays L. The expression abundance of the ZmSBP12 gene is increased to improve the drought resistance of Zea mays L. and reduce the plant and ear heights of Zea mays L., which can be used for the assisted breeding of novel drought-resistant and lodging-resistant Zea mays L. varieties and the breeding of excellent inbred lines and hybrids of Zea mays L.

Abstract: A use of a ZmSBP12 gene in the regulation of drought resistance, plant height, and ear height of Zea mays L. is provided. After the ZmSBP12 gene is over-expressed in Zea mays L., the resulting Zea mays L. mutant plant exhibits increased drought resistance and decreased plant and ear heights. The overexpression of the ZmSBP12 gene leads to increased drought resistance and decreased plant and ear heights, indicating that the ZmSBP12 gene plays a crucial role in the drought resistance and plant type (plant height) of Zea mays L. The expression abundance of the ZmSBP12 gene is increased to improve the drought resistance of Zea mays L. and reduce the plant and ear heights of Zea mays L., which can be used for the assisted breeding of novel drought-resistant and lodging-resistant Zea mays L. varieties and the breeding of excellent inbred lines and hybrids of Zea mays L.

Abstract: Embodiments of the present application disclose methods and apparatuses for determining a bounding box of a target object, media, and devices. The method includes: obtaining attribute information of each of a plurality of key points of a target object; and determining a bounding box position of the target object according to the attribute information of each of the plurality of key points of the target object and to a preset neural network. The implementations of the present application can improve the efficiency and accuracy of determining a bounding box of a target object.

Abstract: A method for object detection includes: obtaining a plurality of to-be-determined targets in a to-be-detected image; determining confidences of the plurality of to-be-determined targets separately belonging to at least one category, determining categories of the plurality of to-be-determined targets according to the confidences, and determining position offset values corresponding to the respective categories of the plurality of to-be-determined targets; using the position offset values corresponding to the respective categories of the plurality of to-be-determined targets as position offset values of the plurality of to-be-determined targets; and determining position information and a category of at least one to-be-determined target in the to-be-detected image according to the categories of the plurality of to-be-determined targets, the position offset values of the plurality of to-be-determined targets, and the confidences of the plurality of to-be-determined targets belonging to the categories thereof.

Abstract: A method for object detection includes: obtaining a plurality of to-be-determined targets in a to-be-detected image; determining confidences of the plurality of to-be-determined targets separately belonging to at least one category, determining categories of the plurality of to-be-determined targets according to the confidences, and determining position offset values corresponding to the respective categories of the plurality of to-be-determined targets; using the position offset values corresponding to the respective categories of the plurality of to-be-determined targets as position offset values of the plurality of to-be-determined targets; and determining position information and a category of at least one to-be-determined target in the to-be-detected image according to the categories of the plurality of to-be-determined targets, the position offset values of the plurality of to-be-determined targets, and the confidences of the plurality of to-be-determined targets belonging to the categories thereof.

Abstract: A method for object detection includes: obtaining a plurality of to-be-determined targets in a to-be-detected image; determining confidences of the plurality of to-be-determined targets separately belonging to at least one category, determining categories of the plurality of to-be-determined targets according to the confidences, and determining position offset values corresponding to the respective categories of the plurality of to-be-determined targets; using the position offset values corresponding to the respective categories of the plurality of to-be-determined targets as position offset values of the plurality of to-be-determined targets; and determining position information and a category of at least one to-be-determined target in the to-be-detected image according to the categories of the plurality of to-be-determined targets, the position offset values of the plurality of to-be-determined targets, and the confidences of the plurality of to-be-determined targets belonging to the categories thereof.

Abstract: Embodiments of the present application disclose methods and apparatuses for determining a bounding box of a target object, media, and devices. The method includes: obtaining attribute information of each of a plurality of key points of a target object; and determining a bounding box position of the target object according to the attribute information of each of the plurality of key points of the target object and to a preset neural network. The implementations of the present application can improve the efficiency and accuracy of determining a bounding box of a target object.