A method of generating training data for re-training an object detecting Neural Network (ODNN), the ODNN having been trained to detect lanes on a digital feed captured by a moving vehicle by determining a portion of the digital image that corresponds to the lanes, the method executable by a computer device, the method comprising: • inputting a first digital image into the ODNN, the first digital image being representative of the digital feed at a first moment in time, the ODNN being configured to detect a given lane on the first digital image by determining a first portion of the first digital image that corresponds to the given lane; • inputting a second digital image into the ODNN, the second digital image being representative of the digital feed at a second moment in time after the first moment in time, the ODNN being configured to detect the given lane on the second digital image by determining a second portion of the second digital image that corresponds to the given lane; • comparing the first portion of the first digital image with the second portion of the second digital image to determine a detection similarity value for the given lane, the detection similarity value being indicative of how similar predictions executed by the ODNN at the first moment in time and the second moment in time in respect to detection of the given lane are; • in response to the detection similarity value being below a pre-determined threshold value, receiving, from a human-assessor for at least one of the first digital image and the second digital image, a human-assessed label indicative of an actual portion occupied by the given lane in the respective one of the first digital image and the second digital image, the actual portion having been identified by the human-assessor based on an artificially-adjusted condition of the road, the artificially-adjusted condition of the road is not an actual condition of the road, the actual condition being that the road has a single lane and the artificially-adjusted condition being that the road artificially has two lanes based on the single lane being broad enough to fit two vehicles side-by-side; and • re-training the ODNN with the human-assessed label and an associated one of at least one of the first digital image and the second digital image, the re-training the ODNN being performed to detect lanes in accordance with artificially-adjusted conditions of roads. • inputting a first digital image into the ODNN, the first digital image being representative of the digital feed at a first moment in time, the ODNN being configured to detect a given lane on the first digital image by determining a first portion of the first digital image that corresponds to the given lane; • inputting a second digital image into the ODNN, the second digital image being representative of the digital feed at a second moment in time after the first moment in time, the ODNN being configured to detect the given lane on the second digital image by determining a second portion of the second digital image that corresponds to the given lane; • comparing the first portion of the first digital image with the second portion of the second digital image to determine a detection similarity value for the given lane, the detection similarity value being indicative of how similar predictions executed by the ODNN at the first moment in time and the second moment in time in respect to detection of the given lane are; • in response to the detection similarity value being below a pre-determined threshold value, receiving, from a human-assessor for at least one of the first digital image and the second digital image, a human-assessed label indicative of an actual portion occupied by the given lane in the respective one of the first digital image and the second digital image, the actual portion having been identified by the human-assessor based on an artificially-adjusted condition of the road, the artificially-adjusted condition of the road is not an actual condition of the road, the actual condition being that the road has a single lane and the artificially-adjusted condition being that the road artificially has two lanes based on the single lane being broad enough to fit two vehicles side-by-side; and • re-training the ODNN with the human-assessed label and an associated one of at least one of the first digital image and the second digital image, the re-training the ODNN being performed to detect lanes in accordance with artificially-adjusted conditions of roads.