Identifying Images with Ladders Using Deep CNN Transfer Learning

Abstract

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.