Object detection from images plays an important role in ITS applications, such as autonomous-driving systems. A concern for recent object detection models based on neural networks is the black box nature of the neural networks, which should be addressed in mission critical ITS applications. In this paper, we propose a framework of partial image retrieval to analyze and debug a target detection model. Given an image with incorrectly detected objects, our method discovers similar images that also contain incorrectly detected objects from a large unlabeled image database, which is relatively easier to collect than annotating all images. These similar images provide us with insights into the misdetection by object detection models, which will be useful in resolving the issue. We qualitatively and quantitatively demonstrate that the proposed framework effectively outputs similar doubtful images using real-world datasets. Specifically, we show that, by querying an image with a false positive detection, we can efficiently find images with false positives from a large unlabeled image database using the proposed framework.


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    Title :

    Finding Misdetections in Unlabeled Image Database: Toward Reliable Object Detection


    Contributors:


    Publication date :

    2021-09-19


    Size :

    2749645 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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