# Effect of Camera Choice on Image-Classification Inference
## 1 Introduction  
There has been much progress in the use of Convolutional Neural Networks (CNNs) as deep learning models for image classification and other computer vision techniques over the past few years [1, 2, 3]. Models such as AlexNet [4], VGG-16 [5], Resnet [6], and MobileNetV2 [7] have been progressively developed to increase the performance and efficiency of image classification. The network models are typically trained and tested on large datasets of clean images. For example, the ImageNet-1k [8, 9] database is composed of 1,281,167 training images, 50,000 validation images and 100,000 test images of 1000 distinct object classes such as furniture, drinking vessels, and various animals and birds. The original metadata for the dataset images, and in particular the source camera and settings (such as exposure time and white balance), are usually unknown or at leastunpublished. This is potentially significant because no two camera types are the same; they have unique characteristics (e.g., field of view, supported resolutions) and unique imperfections (e.g., lens distortion, chromatic aberration) [10] which filter into the trained image classifier. Therefore, there is no guarantee that the datasets are representative of the full range of camera types available, currently or in the future. This raises the question of how significantly the choice of camera type affects the deployment of trained image-classification models in the field. More specifically, is it possible or even likely that the same trained image classifier will give different predictions when using two different camera types? This is the primary objective of investigation in this paper, i.e., to determine whether and to what extent the camera employed to capture an image affects the image-classification output. The answer to this question has very significant implications for many fields, including industrial inspection [11; 12; 13], medical diagnosis [14; 15], and autonomous vehicle operations [16; 17]. For example, consider the consequences of a hypothetical situation where two different camera types are used to acquire comparable images for the diagnosis of a serious human health condition, and the same pretrained CNN model classifies images from one camera as positive for the health condition, but classifies images from the second camera as negative for the health condition. Compared to the analogous topic of examining the robustness of speech recognition in the face of multiple spoken accents [18; 19], the issue of characterizing the robustness of image classification to multiple camera types, each with its own properties, has received relatively little attention. As illustrated in Figure 1, a typical digital camera comprises a lens, image sensor (with Bayer filter), and Image Signal Processor (ISP) [20]. The lens focuses the image on the image sensor, the sensor forms a raw pixelated image, and the ISP provides various processing functions such as de-mosaicing, noise reduction, and compression. Each of these main components of the camera system may have unique properties and imperfections that affect the processed image output. Although one purpose of the ISP is to correct for some of the imperfections of the earlier components, such as lens distortion, the exact capabilities and performance of the ISP will still vary from one camera type to the next. Furthermore, there are properties of a digital camera that depend upon multiple components. One of the most important of these is the Angle of View (AOV) [20], sometimes known as the Field of View (FOV). The AOV describes the angle of observability of the camera and depends both upon the lens and the physical size of the image sensor. A camera with a larger AOV will capture more content than a camera with a smaller AOV. The consequence of this is that the same target object captured by a camera with a larger AOV will appear smaller in the image than when captured by a camera with a smaller AOV, assuming all other parameters (e.g., distance to the object) are kept constant.