Multiple objects can be detected by formatting the input "trainingData" to contain information about multiple object classes. If you refer to the documentation:
you will note that the labeled ground truth "trainingData" has to be formatted as:
Labeled ground truth images, specified as a table with two or more columns. The first column must contain paths and file names to grayscale or truecolor (RGB) images. The remaining columns must contain bounding boxes related to the corresponding image. Each column represents a single object class, such as a car, dog, flower, or stop sign."
In the example linked above the "trainingData" consists of only two columns with the second column representing the object class vehicle. For detecting multiple objects, you have to label your images for the new objects you want to detect and modify the "trainingData" accordingly.
