Relabeling Method for Improving Vehicle Part Detection | IEEE Conference Publication | IEEE Xplore

Relabeling Method for Improving Vehicle Part Detection


Abstract:

The lightweight deep learning detection method does not properly distinguish partial objects that are named differently depending on their location but has the same shape...Show More

Abstract:

The lightweight deep learning detection method does not properly distinguish partial objects that are named differently depending on their location but has the same shape and appearance like a tire. To solve the problem, this paper introduces a method to relabel the detection result of vehicle parts. We define vehicles parts with super-class and sub-class according to the shape and location, and then detect super-classes. For relabeling super-class to sub-class, we classify vehicle viewing direction, and generate a set of sub-class combinations from super-classes. The spatial distributions among the detected partial objects are analyzed using the likelihood. Then, the labels of the detected parts are determined. We tested our method with self-collected and open dataset, and achieved a mAP value of 87.7%, which is about 11 % better than tiny YOLO v4.
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 25 November 2022
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Conference Location: Jeju Island, Korea, Republic of

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