A method for training a system for automatically detecting a defect of a blade of a turbomachine comprising a platform intended to receive a blade, at least one image-acquiring means, at least one light source the luminous intensity of which may vary, at least one moving means configured to move the platform with respect to said at least one image-acquiring means or to move said at least one image-acquiring means with respect to the platform, and a processing unit configured to receive acquired images and associate them with the items of information relating to the luminous intensity of said at least one light source and to the relative position of the platform with respect to said at least one image-acquiring device during the image acquisition, the training method comprising, for each of the blades to be studied a set of blades with surface defects that have already been identified, the following steps: - defining useful areas on the blade, - producing, for each separate relative position of the platform with respect to said at least one image-acquiring device, a plurality of acquired images of the useful area of the blade, each of the acquired images for one and the same relative position being produced with a different luminous intensity, - dividing each image of the useful area from one acquired image into a plurality of images of sub-areas, - determining a plurality of pairs of pixel intensity bounds, each pair comprising a minimum intensity and a maximum intensity, - for each pair of bounds: a. for each sub-area of a useful area, determining the number of pixels contained in each of the images of the sub-area having an intensity between the minimum intensity and the maximum intensity of the pair of bounds, then b. for each given relative position, for each sub-area of said relative position, selecting a single image, the selected image corresponding to the image comprising the most pixels, the intensity of which is between said minimum and maximum intensities of the pair of bounds, then c. for each given relative position, for each sub-area, inspecting each corresponding selected image for this sub-area by a classifying convolutional neural network to detect the presence of any defects, then d. checking the detections made by the classifying convolutional neural network to determine whether the detected defects are real or false, and e. computing the detection efficiency for the given pair of bounds over the useful area on the basis of all the given relative positions, the detection efficiency depending on the ratio of the number of proven detections to the number of false detections, - selecting the pair of bounds having the best detection efficiency. - defining useful areas on the blade, - producing, for each separate relative position of the platform with respect to said at least one image-acquiring device, a plurality of acquired images of the useful area of the blade, each of the acquired images for one and the same relative position being produced with a different luminous intensity, - dividing each image of the useful area from one acquired image into a plurality of images of sub-areas, - determining a plurality of pairs of pixel intensity bounds, each pair comprising a minimum intensity and a maximum intensity, - for each pair of bounds: a. for each sub-area of a useful area, determining the number of pixels contained in each of the images of the sub-area having an intensity between the minimum intensity and the maximum intensity of the pair of bounds, then b. for each given relative position, for each sub-area of said relative position, selecting a single image, the selected image corresponding to the image comprising the most pixels, the intensity of which is between said minimum and maximum intensities of the pair of bounds, then c. for each given relative position, for each sub-area, inspecting each corresponding selected image for this sub-area by a classifying convolutional neural network to detect the presence of any defects, then d. checking the detections made by the classifying convolutional neural network to determine whether the detected defects are real or false, and e. computing the detection efficiency for the given pair of bounds over the useful area on the basis of all the given relative positions, the detection efficiency depending on the ratio of the number of proven detections to the number of false detections, a. for each sub-area of a useful area, determining the number of pixels contained in each of the images of the sub-area having an intensity between the minimum intensity and the maximum intensity of the pair of bounds, then b. for each given relative position, for each sub-area of said relative position, selecting a single image, the selected image corresponding to the image comprising the most pixels, the intensity of which is between said minimum and maximum intensities of the pair of bounds, then c. for each given relative position, for each sub-area, inspecting each corresponding selected image for this sub-area by a classifying convolutional neural network to detect the presence of any defects, then d. checking the detections made by the classifying convolutional neural network to determine whether the detected defects are real or false, and e. computing the detection efficiency for the given pair of bounds over the useful area on the basis of all the given relative positions, the detection efficiency depending on the ratio of the number of proven detections to the number of false detections, - selecting the pair of bounds having the best detection efficiency.