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The railway maintenance is a particular application context required in order to prevent any dangerous situation.With the growing of the high-speed railway traffic, automatic inspection systems able to detect rail defects, sleepers' anomalies, as well as missing fastening elements, become strategic since they could increase the ability in the detection of defects and reduce the inspection time in order to guarantee more frequent maintenance of the railway network.This paper presents a patented fully automatic and configurable real-time vision system able to detect the presence/absence of the fastening bolts that fix the rails to the sleepers. It gets an accuracy of 99.9%, and, thanks to its parallel processing allowed by a Graphic Processing Unit, reaches an average throughput of 187 km/h, speeding up of about 287 % the performance of a quadcore CPU implementation.

The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagnosis (CAD) system to assess global glomerulosclerosis. The proposed tool is based on Convolutional Neural Networks (CNNs). In particular, the authors considered approaches based on Semantic Segmentation networks, such as SegNet and DeepLab v3+. The dataset has been provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital, and it is composed of 26 kidney biopsies coming from 19 donors. The dataset contains 2344 non-sclerotic glomeruli and 428 sclerotic glomeruli. The proposed model consents to achieve promising results in the task of automatically detecting and classifying glomeruli, thus easing the burden of pathologists. We get high performance both at pixel-level, achieving mean F-score higher than 0.81, and Weighted Intersection over Union (IoU) higher than 0.97 for both SegNet and Deeplab v3+ approaches, and at object detection level, achieving 0.924 as best F-score for non-sclerotic glomeruli and 0.730 as best F-score for sclerotic glomeruli.

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