Robustness of Image Classification Using CNNs in Adverse Conditions

Abstract: The usage of convolutional neural networks (CNNs) has revolutionized the field of computer vision. Though the algorithms used in image recognition have improved significantly in the past decade, they are still limited by the availability of training data. This paper aims to gain a better understanding of how limitations in the training data might affect the performance of the system. A robustness study was conducted. The study utilizes three different image datasets; pre-training CNN models on the ImageNet or CIFAR-10 datasets, and then training on the MAdWeather dataset, whose main characteristic is containing images with differing levels of obscurity in front of the objects in the images. The MAdWeather dataset is used in order to test how accurately a model can identify images that differ from its training dataset. The study shows that CNNs performance on one condition does not translate well to other conditions.