<div class="section abstract"><div class="htmlview paragraph">Adverse weather conditions degrade the quality of images used in vision-based advanced driver assistance systems (ADAS) and autonomous driving algorithms. Adherent raindrops onto a vehicle’s windshield occlude parts of the input image and blur background texture in regions covered by them. Rain also changes image intensity and disturbs chromatic properties of color images. In this work, we collected a dataset using a camera mounted behind a windshield at different rain intensities. The data was processed to generate a set of distorted images by adherent raindrops along with ground truth data of clear images (just after a windshield wipe). We quantitatively evaluated the amount of distortion caused by the raindrops, using the Normalized Cross-Correlation and Structural Similarity methods. While most prior work in the field of rain detection and removal focuses on the image restoration aspects, they typically do not provide quantitative measures to the effect of degradation of input image quality on the performance of image-based algorithms. We quantitatively evaluated the effect of raindrop distortion on deep-learning-based object detection algorithms by comparing the detectors’ performance on the distorted images to the clear images. State-of-the-art detector algorithms were selected and used, namely, Faster Region-based Convolution Neural Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). For the overall performance of the object detection and classification algorithms, we used standard accuracy, precision, and recall measures.</div></div>

Research on the effect of adverse weather conditions on the performance of vision-based algorithms for automotive tasks has had significant interest. It is generally accepted that adverse weather conditions reduce the quality of captured images and have a detrimental effect on the performance of algorithms that rely on these images. Rain is a common and significant source of image quality degradation. Adherent rain on a vehicle’s windshield in the camera’s field of view causes distortion that affects a wide range of essential automotive perception tasks, such as object recognition, traffic sign recognition, localization, mapping, and other advanced driver assist systems (ADAS) and self-driving features. As rain is a common occurrence and as these systems are safety-critical, algorithm reliability in the presence of rain and potential countermeasures must be well understood. This survey paper describes the main techniques for detecting and removing adherent raindrops from images that accumulate on the protective cover of cameras.

Long-acting testosterone replacement therapy (TRT) suppresses spermatogenesis.A short-acting TRT, Natesto, maintains spermatogenesis in some men. This study evaluated hormonal and semen parameters converting men from long-acting TRT to Natesto. Baseline hormones, again on long-acting TRT and 1 month after converting to Natesto, as well as semen parameters 3 months after converting to Natesto were assessed. Twenty-seven men were directly converted from longacting forms of TRT to Natesto. Mean duration on long-acting TRT was 24.3 ± 19 months. Testosterone levels were similar on long-acting forms of TRT

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