Search Results (3)

Alpine skiing is characterized by specific and dynamic conditions and demands constant processing of visual information and fast decision-making. A fast response time is necessary for protective movements which reduce the number and severity of additional head impacts. The apparent detriments to visual performance caused by protective headgear are concerning and should be considered moving forward in recreational alpine skiing. The aim of this study was to examine the effects of wearing the three most common combinations of protective headgear in skiing on the timing of visual stimuli perception and adequate response when simulating on-the-slope situations. The sample consisted of 45 recreational-level skiers (27 M, 18 F; age 30.6 ± 8.19 years) who had finished basic alpine skiing school, had been skiing 6–10 years continuously, and were students of Faculty of Kinesiology, University of Zagreb. They did not report any serious medical conditions regarding vision. The overall testing was conducted in the winter season during January and February of 2022. Reaction time on perceived visual stimuli was observed in a way that a skier was approaching behind a participant’s back from both the left and right side. A 2 × 3 (helmet*condition) mixed-model repeated-measures ANOVA was used to determine differences between helmet users and non-users in each tested condition. When observing the results, it was confirmed that the response time of the participants was the slowest when wearing a ski helmet and goggles combined. Furthermore, one of the most important findings was the determined differences in reaction time between helmet users and non-users, i.e., prior helmet users tended to react faster to the upcoming visual stimuli when wearing combined ski helmet and goggles. In the design and construction of the goggles, it is also necessary to pay attention to reducing the thickness of the frame in order to reduce the distance between the eye and the lens, which consequently reduces interference in the peripheral parts of the field of vision. In future studies, the same testing protocol with all the possible combinations of wearing a ski cap, a helmet, sunglasses, and goggles is necessary to gain a clearer insight into the effect of each item of headgear separately and in various combinations. Full article

Ski goggles help protect the eyes and enhance eyesight. The most important part of ski goggles is their lenses. The quality of the lenses has leaped with technological advances, but there are still defects on their surface during manufacturing. This study develops a deep learning-based defect detection system for ski goggles lenses. The first step is to design the image acquisition model that combines cameras and light sources. This step aims to capture clear and high-resolution images on the entire surface of the lenses. Next, defect categories are identified, including scratches, watermarks, spotlight, stains, dust-line, and dust-spot. They are labeled to create the ski goggles lenses defect dataset. Finally, the defects are automatically detected by fine-tuning the mobile-friendly object detection model. The mentioned defect detection model is the MobileNetV3 backbone used in a feature pyramid network (FPN) along with the Faster-RCNN detector. The fine-tuning includes: replacing the default ResNet50 backbone with a combination of MobileNetV3 and FPN; adjusting the hyper-parameter of the region proposal network (RPN) to suit the tiny defects; and reducing the number of the output channel in FPN to increase computational performance. Our experiments demonstrate the effectiveness of defect detection; additionally, the inference speed is fast. The defect detection accuracy achieves a mean average precision (mAP) of 55%. The work automatically integrates all steps, from capturing images to defect detection. Furthermore, the lens defect dataset is publicly available to the research community on GitHub. The repository address can be found in the Data Availability Statement section. Full article

No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries. Full article