Reducing false detections for night vision cameras

A computer-implemented method comprising:

• for each of a plurality of training images each of which are associated with a classification type from two or more classification types, the plurality of training images including at least one image for each of the two or more classification types:
• providing, to a neural network of an image classifier that is trained to detect objects of the two or more classification types, a feature vector for the respective training image;
• receiving, from the neural network, an output vector that indicates, for each of the two or more classification types, a likelihood that the respective training image depicts an object of the corresponding classification type;
• accessing, from two or more ground truth vectors each for one of the two or more classification types, a ground truth vector for the classification type of an object depicted in the respective training image;
• and
• adjusting one or more weights in the neural network using a combination of the output vector and the ground truth vector for the classification type of the objected depicted in the respective training image;
• and
• storing, in a memory, the image classifier that includes the neural network for use by a camera to classify objects detected in one or more images captured by the camera.

A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• providing, to a neural network being trained to detect objects of interest, a training image that depicts an object;
• receiving, from the neural network, an indication that the object is a first class of object of non-interest;
• determining that the object is a second class of object of non-interest and was incorrectly classified by the neural network as the first class of object of non-interest;
• and
• weighting the neural network towards correctly classifying the object as the second class of object of non-interest.

A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

• for each of a plurality of training images each of which are associated with a classification type from two or more classification types, the plurality of training images including at least one image for each of the two or more classification types:
• providing, to a neural network of an image classifier that is trained to detect objects of the two or more classification types, a feature vector for the respective training image;
• receiving, from the neural network, an output vector that indicates, for each of the two or more classification types, a likelihood that the respective training image depicts an object of the corresponding classification type;
• accessing, from two or more ground truth vectors each for one of the two or more classification types, a ground truth vector for the classification type of an object depicted in the respective training image;
• and
• adjusting one or more weights in the neural network using a combination of the output vector and the ground truth vector for the classification type of the objected depicted in the respective training image;
• and
• storing, in a memory, the image classifier that includes the neural network for use by a camera to classify objects detected in one or more images captured by the camera.