Security system with explainable false positives

A computer system comprising:

• a detection engine configured to:
• apply a first object detection model to an image, where the first object detection model is trained to detect a target object and output a class activation map (CAM) for the image;
• a model diagnostic engine configured to:
• generate an overlay image based on the CAM and the image;
• and
• a GUI engine configured to:
• responsive to a determination that the target object is depicted in the image, cause a first notification to be generated at a client device, the first notification including the overlay image and a request for user feedback representative of a first metric of accuracy of the first object detection model;
• wherein the model diagnostic engine is further configured to:
• generate, using the user feedback, a CAM score representative of a second metric of accuracy of the first object detection model,
• determine a model score of the first object detection model using the CAM score, and
• execute a comparison of the model score of the first object detection model to a model score of a second object detection model trained to detect the target object;
• and
• wherein the GUI engine is further configured to:
• cause a second notification to be generated at the client device, the second notification including a recommendation to apply one of the first object detection model or second object detection model based on the comparison.

A method comprising:

• applying a first object detection model to an image, where the first object detection model is trained to detect a target object and output a class activation map (CAM) for the image;
• generating an overlay image based on the CAM and the image;
• responsive to a determination that the target object is depicted in the image, causing a first notification to be generated at a client device, the first notification including the overlay image and a request for user feedback representative of a first metric of accuracy of the first object detection model;
• generating, using the user feedback, a CAM score representative of a second metric of accuracy of the first object detection model;
• responsive to determining that the CAM score is lower than a threshold CAM score, causing a second notification to be generated at the client device, the second notification including a request for a training image of the target object depicted through a different photographic attribute than a depiction of the target object in the image;
• and
• re-training the first object detection model using the training image.