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Detection and Recovery of Errors in Object Detection with Faulty Convolutional Neural Network

Giuseppe Palumbo

Detection and Recovery of Errors in Object Detection with Faulty Convolutional Neural Network.

Rel. Maurizio Martina, Guido Masera, Emanuele Valpreda, Claudio Passerone, Pierpaolo Mori'. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2022

Abstract:

Convolutional neural networks (CNN) are extensively used in computer vision tasks such as image classification and object detection. Depending on the field of use, the decision-making process based on a CNN may be a sensitive issue because it affects the behavior of a system that works in critical applications such as autonomous driving systems. For that reason, anomalies in the CNN estimations have to be confined. This thesis proposes a solution to detect and recover errors in a CNN's output. The errors can be of two types: miss detection and wrong detection. The proposed algorithm leverages the temporal correlation among consecutive CNN outputs to generate interesting features that suggest whether an error occurs. Miss detections are solved by recovering the lost objects with prediction algorithms, whereas wrong detections are solved by canceling non-existent objects with a filter. The proposed method has been implemented as a CNN-agnostic algorithm called Bounding Box Recovery (BB-Rec) that uses the same inputs and outputs processed and generated by the CNN. In order to evaluate the performance, precision and recall of the stand-alone CNN and of the same CNN supported by BB-Rec have been computed and compared using MOTChallange dataset and py-motmetrics benchmark. Performance gains are achieved by the supported-CNN that presents, as the main result, improvements in recall combined, however, with a decrease in precision. These results show that the proposed approach can effectively enhance CNN's recall metric, recovering lost bounding boxes.

Relatori: Maurizio Martina, Guido Masera, Emanuele Valpreda, Claudio Passerone, Pierpaolo Mori'
Anno accademico: 2022/23
Tipo di pubblicazione: Elettronica
Numero di pagine: 91
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-29 - INGEGNERIA ELETTRONICA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/24561
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