Object Detection Model Error Prediction Accuracy

The patent segments the detection task into two independent models: an object detection model for identifying target objects and an error prediction model for identifying error objects. This segmentation allows each model to specialize in its specific function, with the error prediction model specifically trained to detect regions that are likely to be false positives. By separating these functions, the system can independently optimize each model's performance while using the error prediction results to filter false positives from the object detection results.

The error prediction model acts as an intermediary between the object detection model and the final detection results. It processes the detected target candidate objects and determines which ones are likely to be error objects based on overlapping regions. This intermediary layer provides an additional verification step that filters out false positives before finalizing the detection results, thereby improving overall detection accuracy without compromising the coverage of the object detection model.

The patent merges the feature extraction functionality of both models by having them share common feature extraction layers. This allows the object detection model and error prediction model to leverage the same learned features from the input image, reducing redundant computation. The shared feature extraction backbone processes the image once, and both models utilize these features for their respective tasks, thereby reducing overall computational complexity while maintaining the benefits of error prediction.

The error prediction model applies partial action by focusing only on the regions identified as target candidates by the object detection model. Instead of processing the entire image, the error prediction model concentrates its computational resources on evaluating the likelihood of error objects in the already-detected regions. This partial processing approach reduces the computational burden of the error prediction model while still achieving the goal of filtering false positives effectively.