A4 Artikkeli konferenssijulkaisussa
Identifying Images with Ladders Using Deep CNN Transfer Learning (2020)

Pandey, G., Baranwal, A., & Semenov, A. (2020). Identifying Images with Ladders Using Deep CNN Transfer Learning. In I. Czarnowski, R. J. Howlett, & L. C. Jain (Eds.), Intelligent Decision Technologies 2019 : Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 1 (pp. 143-153). Springer. Smart Innovation, Systems and Technologies, 142. https://doi.org/10.1007/978-981-13-8311-3_13

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Pandey, Gaurav; Baranwal, Arvind; Semenov, Alexander

Emojulkaisu: Intelligent Decision Technologies 2019 : Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 1

Emojulkaisun toimittajat: Czarnowski, Ireneusz; Howlett, Robert J.; Jain, Lakhmi C.

Konferenssi:

Konferenssin paikka ja aika: Malta, 17.-19.6.2019

ISBN: 978-981-13-8310-6

eISBN: 978-981-13-8311-3

Lehti tai sarja: Smart Innovation, Systems and Technologies

ISSN: 2190-3018

eISSN: 2190-3026

Julkaisuvuosi: 2020

Sarjan numero: 142

Artikkelin sivunumerot: 143-153

Kirjan kokonaissivumäärä: 354

Kustantaja: Springer

Kustannuspaikka: Singapore

Julkaisumaa: Singapore

Julkaisun kieli: englanti

DOI: https://doi.org/10.1007/978-981-13-8311-3_13

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

Deep Convolutional Neural Networks (CNNs) as well as transfer learning using their pre-trained models often find applications in image classification tasks. In this paper, we explore the utilization of pre-trained CNNs for identifying images containing ladders. We target a particular use case, where an insurance firm, in order to decide the price for workers’ compensation insurance for its client companies, would like to assess the risk involved in their workplace environments. For this, the workplace images provided by the client companies can be utilized and the presence of ladders in such images can be considered as a workplace hazard and therefore an indicator of risk. To this end, we explore the utilization of pre-trained CNN models: VGG-16 and VGG-19, to extract features from images in a training set, that in turn are used to train a binary classifier (classifying an image as ladder and no ladder). The trained binary classifier can then be used for future predictions. Moreover, we explore the effect of including standard image augmentation techniques to enrich the training set. We also explore improving classification predictions by combining predictions generated by two individual binary classifiers that utilize features obtained from pre-trained VGG-16 and VGG-19 models. Our experimental results compare accuracies of classifiers that utilize features obtained using pre-trained VGG-16 and VGG-19 models. Furthermore, we analyze improvements in accuracies achieved on using image augmentation techniques as well as on combining predictions from VGG-16 and VGG-19 transfer learning based binary classifiers.

YSO-asiasanat: hahmontunnistus (tietotekniikka); koneoppiminen

Vapaat asiasanat: CNN; image classification; deep learning

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1