Methods and systems for identifying potential vehicle defects

A method for using a trained machine learning (ML) model to detect presence of rust on an undercarriage of a vehicle, the method comprising:

• using at least one computer hardware processor to perform:
• obtaining at least one image of the undercarriage of the vehicle and metadata indicating one or more properties of the vehicle;
• processing the at least one image and the metadata to detect presence of the rust, the processing comprising:
• generating image input from the at least one image;
• generating metadata input from the metadata;
• and
• processing the image input and the metadata input using the trained ML model to obtain output indicative of presence or absence of the rust on the undercarriage of the vehicle.

A system, comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for using a trained machine learning (ML) model to detect presence of rust on an undercarriage of a vehicle, the method comprising:
• obtaining at least one image of the undercarriage of the vehicle and metadata indicating one or more properties of the vehicle;
• and
• processing the at least one image and the metadata to detect presence of the rust, the processing comprising:
• generating image input from the at least one image;
• generating metadata input from the metadata;
• and
• processing the image input and the metadata input using the trained ML model to obtain output indicative of presence or absence of the rust on the undercarriage of the vehicle.

At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for using a trained machine learning (ML) model to detect presence of rust on an undercarriage of a vehicle, the method comprising:

• obtaining at least one image of the undercarriage of the vehicle and metadata indicating one or more properties of the vehicle;
• and
• processing the at least one image and the metadata to detect presence of the rust, the processing comprising:
• generating image input from the at least one image;
• generating metadata input from the metadata;
• and
• processing the image input and the metadata input using the trained ML model to obtain output indicative of presence or absence of the rust on the undercarriage of the vehicle.