Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157090
Title: Towards out-of-distribution detection for object detection networks
Authors: Kanodia, Ritwik
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Kanodia, R. (2022). Towards out-of-distribution detection for object detection networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157090
Abstract: Many studies have recently been published on recognizing when a classification neural network is provided with data that does not fit into one of the class labels learnt during training. These so-called out-of-distribution (OOD) detection approaches have the potential to improve system safety in situations when unexpected or new inputs might cause mistakes that jeopardize human life. These approaches would particularly be able to aid autonomous vehicles if they could be used to detect and pinpoint anomalous objects in a driving environment, allowing the system to either fail gracefully or to treat such objects with extreme caution. We dive deep into an existing and promising OOD Detection method from the image classification literature called 'Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output' and explore how it can be modified for application in object detection networks. We then apply our approach to the YOLOv3 object detector and evaluate it across multiple metrics to empirically prove the effectiveness of the approach.
URI: https://hdl.handle.net/10356/157090
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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