# Effect of Camera Choice on Image-Classification Inference
## 2 Related Work  
A large-scale investigation into the effect of image quality on image classification was conducted in [21]. The authors used a subset of the validation images from the ImageNet-1k database and digitally introduced varying levels of quality distortions, including blur, noise, contrast, and compression. They then classified the manipulated images using pretrained Caffe reference [22], VGG-CNN-S [23], VGG-16 [5], and GoogleNet [24] models, with the model weights taken from the Caffe library. The image-classification performance was found to be most sensitive to the quality distortions of blur and noise for all models. The authors noted that this extended range of quality distortions may be found in adversarial images [25], which are images that are purposely manipulated to fool an image classifier but are unlikely to be found in non-manipulated images. However, the normal image acquisition process can introduce both blur (e.g., when an image is taken out of focus) and noise (e.g., due to a low-quality image sensor). This motivates the current study discussed in this paper, where we attempt to find differences in the image-classification output from images of the same item captured by different cameras under the same environmental conditions. The research in [26] aimed at training robust image classifiers to handle failures in the image acquisition process. These failures were classified as internal (e.g., black pixels due to a poor or failed image sensor), external (e.g., scratched lens), or environmental (e.g., ice, rain, or condensation on the lens) [27, 28]. The authors augmented three well-known Traffic Signal Recognition (TSR) datasets with additional images corresponding to a total of 13 specific visual camera failures, then trained and tested AlexNet [4], MobileNetV2 [7], and Inceptionv3 [29] classifiers based on the augmented dataset. The results demonstrated significantly improved classification accuracy using this approach. Our study is somewhat different in scope in that we are examining the effect of the camera type employed to acquire an image on the image classifier output based upon the fact that the different cameras have different characteristics. We do not consider visual camera failures per se, and we do not train models (rather, we use a pretrained classifier corresponding to the ImageNet-1k database). Nevertheless, it may be that retraining of classifiers based upon the data augmentation proposed in [26] may help to mitigate differences in image-classification output when different cameras are used to capture images. However, this is a topic for further research. The study discussed in [30] confirmed that degradations in image quality have a significant impact on image-classification performance. Building on some previous research [31, 32, 33], the authors examined nine specific degradations, including hazy images, motion blur, out-of-focus blur, underwater images, fish-eye camera images, very low-resolution images, and salt-and-pepper noise using AlexNet [4], VGG-16 [5], and Resnet [6] image classifiers. The salient aspect of this research was that an attempt was made to remove the degradations using accepted image processing techniques to restore the clean images prior to image classification. However, it was discovered this only marginally mitigated the drop in image-classification performance. A recent systematic review of research into the robustness of computer vision to natural image degradations rather than intentional adversarial image degradations is provided in [10]. This highlights that natural variations in image quality (for example, as produced by different camera types) have received relatively little interest compared to adversarial perturbations. This is the case even though studies [28, 34, 35, 36, 37] show naturally degraded images can result in a 30-40% decrease in image-classification accuracy, which is very significant when computer vision is integrated into safety critical systems such as autonomous vehicles.