Ball Detection Using Deep Learning Implemented on an Educational Robot Based on Raspberry Pi.

RoboCupJunior is a project-oriented competition for primary and secondary school students that promotes robotics, computer science and programing. Through real life scenarios, students are encouraged to engage in robotics in order to help people. One of the popular categories is Rescue Line, in which an autonomous robot has to find and rescue victims. The victim is in the shape of a silver ball that reflects light and is electrically conductive. The robot should find the victim and place it in the evacuation zone. Teams mostly detect victims (balls) using random walk or distant sensors. In this preliminary study, we explored the possibility of using a camera, Hough transform (HT) and deep learning methods for finding and locating balls with the educational mobile robot Fischertechnik with Raspberry Pi (RPi). We trained, tested and validated the performance of different algorithms (convolutional neural networks for object detection and U-NET architecture for sematic segmentation) on a handmade dataset made of images of balls in different light conditions and surroundings. RESNET50 was the most accurate, and MOBILENET_V3_LARGE_320 was the fastest object detection method, while EFFICIENTNET-B0 proved to be the most accurate, and MOBILENET_V2 was the fastest semantic segmentation method on the RPi. HT was by far the fastest method, but produced significantly worse results. These methods were then implemented on a robot and tested in a simplified environment (one silver ball with white surroundings and different light conditions) where HT had the best ratio of speed and accuracy (4.71 s, DICE 0.7989, IoU 0.6651). The results show that microcomputers without GPUs are still too weak for complicated deep learning algorithms in real-time situations, although these algorithms show much higher accuracy in complicated environment situations.

Relative intersection of confidence intervals rule for sharper restoration of soft x-ray images.

We present a novel method for restoration of images of nanostructures obtained with a soft-ray microscope that uses a 46.9 nm soft x-ray laser microscope for illumination. To suppress the noise and to preserve the image sharpness, we develop a method based on pixel adaptive zero-order modeling of the observed object. Neighboring areas of each pixel are selected using the relative intersection of confidence intervals rule and used for restoration. Due to the non-uniform distribution of noise in the images, we use robust spatial noise modeling. The method provides sharp restored images-sharper than competitive approaches. The sharpness is measured using local phase coherence in the complex wavelet transform domain and shows visible improvement of the novel method.