1. A method comprising detecting the presence of a first object and a second object in at least one image using an object detection algorithm that uses training image data to train the detection algorithm to detect a given object based at least in part on a similarity of appearance of image data in a corresponding area of a first image of the at least one image to data derived at least in part from the training image data, the method comprising: providing a first output indicative of a relatively high similarity of appearance of at least part of a first area of the first image to data derived at least in part from the training image data, the first output indicating that a first detection confidence is sufficiently high to indicate that a first object is likely to have been detected in the first area; determining a characteristic of a second area, of the first image or of a second image of the at least one image, in which an object may be detected, the characteristic being derived from data relating to the first area and being capable of indicating a likelihood of presence of a second object in the second area; attempting to detect the presence of the second object in the second area; responsive to a detection of the second object based on a similarity of appearance of at least part of the second area to data derived at least in part from the training image data, determining based on the determined characteristic whether to provide a second output indicating that a second detection confidence is sufficiently high to indicate that the second object is likely to be present in the second area; outputting image data associated with the second area, the outputted image data being for use in further training of the object detection algorithm; adding the outputted image data to a dataset that is used to train the object detection algorithm, thereby to generate a revised dataset; training the object detection algorithm with the revised dataset, thereby to generate a revised trained object detection algorithm; and testing a performance of the object detection algorithm against that of the revised trained object detection algorithm, wherein: the testing is based on determining a ratio of a number of first outputs to a number of second outputs, the characteristic is proximity to a position, the number of first outputs is the number of first objects having the first detection confidence and being within a predefined distance of the position, and the number of second outputs is the number of second objects having the second detection confidence and being within the predefined distance of the position.