Abstract: According to various embodiments, techniques and mechanisms are provided to enhance damage detection and assessment for objects such as vehicles. In some implementations, a striped lighting tunnel is configured with cameras for capture of object or vehicle surface images when illuminated by striped lighting. The vehicle surface images may be captured from a variety of perspectives. Damage assessed is mapped to multiple views of a vehicle. In some embodiments, a damage likelihood score is generated and if the damage likelihood score reaches a threshold, a heatmap visual representation may be generated and included in one or more views to present the type, likelihood, and extent of damage.

Abstract: A computing system configured to process a plurality of intermediate outputs from machine learning models to generate final outputs may be maintained. A combined confidence metric that reflects a probability that the final outputs are accurate may be determined based on the intermediate outputs. Suitability of the combined outputs as inputs for a further model may be determined using the combined confidence metrics. The suitable combined outputs may be caused to be used as inputs for the further model.

Abstract: According to various embodiments, techniques and mechanisms are provided to improve striped lighting damage detection. In some implementations, a striped lighting tunnel is configured with cameras for capture of object or vehicle surface images when illuminated by striped lighting and when illuminated by uniform panel lighting. The vehicle surface images may be captured from a variety of perspectives. The vehicle surface images can be analyzed to generate vehicle object models or can be analyzed by using these vehicle object models. Damage may be determined using the vehicle surface images to identify the type, likelihood, and extent of damage.

Abstract: A computing system configured to process a plurality of intermediate outputs from machine learning models to generate final outputs may be maintained. A combined confidence metric that reflects a probability that the final outputs are accurate may be determined based on the intermediate outputs. Outputs associated with combined confidence metrics that are below the threshold may be caused to be discarded.

Abstract: A computing system configured to process a plurality of intermediate outputs from machine learning models to generate final outputs may be maintained. A combined confidence metric that reflects a probability that the final outputs are accurate may be determined based on the intermediate outputs. A comprehensive damage assessment may be presented. The comprehensive damage assessment may include identified damage having combined confidence metrics determined to be above a threshold.