Fisheye cameras have recently became very popular in computer vision applications due to their wide field of view. In addition to a better overview of the surrounding area, they enable to capture objects at extremely close ranges. These advantages come at a cost of strong image distortion, which cannot be removed completely maintaining image continuity. This complicates the use of traditional computer vision algorithms, which expect a single image as an input. This paper presents a performance evaluation of neural network algorithms for object detection and segmentation on fisheye camera images. Three approaches are evaluated: semantic image segmentation with Fully Convolutional Network (FCN) [13], a fully convolutional approach to instance segmentation with U-Net [18] and a region-based approach to instance segmentation with Mask R-CNN [10]. All of these networks successfully solved the task. However, as they were designed to different purposes, each of them has its own strengths and shortcomings. These three approaches are used to perform euro container image segmentation task. An image dataset was created in order to train and evaluate these algorithms. Huge part of this dataset was generated artificially, which simplified the task of ground truth labeling. The power of neural networks enable for fast and reliable image segmentation. As to our knowledge, this is the first neural networks application for euro container fisheye image detection and segmentation.