1. A method for image analysis, comprising: recording an image at a vehicle system mounted to a vehicle; automatically detecting an object within the image with a first module; automatically defining a first bounding box about the detected object; modifying the image with the first bounding box for the detected object to generate a first modified image; at a first verification module associated with the first module, labeling the first bounding box within the modified image as one of a false positive, a false negative, a true positive, and a true negative detected object; training the first module with the label for the first bounding box; automatically classifying the detected object with a second module, wherein classifying the detected object comprises automatically assigning an object class to the detected object, wherein the object class is one of a plurality of object classes, to generate a classified object; modifying the image with a second bounding box for the classified object to generate a second modified image, wherein the second modified image comprises a set of pixels; at a second verification module associated with the object class, labeling the second bounding box within the second modified image as one of a false positive, a false negative, a true positive, and a true negative for the object class, wherein the second verification module is one of a plurality of verification modules, each associated with a different object class of the plurality of object classes, wherein labeling the second bounding box comprises: automatically determining a horizon in the second modified image based on image fiducials, and automatically labeling the detected object as a false positive based on at least a portion of the second bounding box comprising pixels of the set of pixels located above the horizon; training the second module with the label for the second bounding box within the second modified image; and automatically detecting and classifying objects within a second image, recorded at the vehicle system, with the trained first module and trained second module, respectively.