A New Loss Function for Simultaneous Object Localization and Classification

Ander Sanchez-Chica, Beñat Ugartemendia-Telleria, Ekaitz Zulueta (), Unai Fernandez-Gamiz and Javier Maria Gomez-Hidalgo
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Ander Sanchez-Chica: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Beñat Ugartemendia-Telleria: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Ekaitz Zulueta: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Unai Fernandez-Gamiz: Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Javier Maria Gomez-Hidalgo: MERCEDES BENZ España, Las arenas 1, 10152 Vitoria-Gasteiz, Spain

Mathematics, 2023, vol. 11, issue 5, 1-13

Abstract: Robots play a pivotal role in the manufacturing industry. This has led to the development of computer vision. Since AlexNet won ILSVRC, convolutional neural networks (CNNs) have achieved state-of-the-art status in this area. In this work, a novel method is proposed to simultaneously detect and predict the localization of objects using a custom loop method and a CNN, performing two of the most important tasks in computer vision with a single method. Two different loss functions are proposed to evaluate the method and compare the results. The obtained results show that the network is able to perform both tasks accurately, classifying images correctly and locating objects precisely. Regarding the loss functions, when the target classification values are computed, the network performs better in the localization task. Following this work, improvements are expected to be made in the localization task of networks by refining the training processes of the networks and loss functions.

Keywords: image classification; object detection; deep learning; deep convolutional neural networks; computer vision; custom training loop (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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