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A New Loss Function for Simultaneous Object Localization and Classification

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ander Sanchez-Chica & Beñat Ugartemendia-Telleria & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Maria Gomez-Hidalgo, 2023. "A New Loss Function for Simultaneous Object Localization and Classification," Mathematics, MDPI, vol. 11(5), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1205-:d:1084375
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    References listed on IDEAS

    as
    1. Daniel Teso-Fz-Betoño & Ekaitz Zulueta & Ander Sánchez-Chica & Unai Fernandez-Gamiz & Aitor Saenz-Aguirre, 2020. "Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot," Mathematics, MDPI, vol. 8(5), pages 1-19, May.
    2. Ibai Inziarte-Hidalgo & Irantzu Uriarte & Unai Fernandez-Gamiz & Gorka Sorrosal & Ekaitz Zulueta, 2023. "Robotic-Arm-Based Force Control in Neurosurgical Practice," Mathematics, MDPI, vol. 11(4), pages 1-12, February.
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