Also, this mandates the assessment of familiar and latest deep neural network models such as CenterNet Hourglass, EfficientDet, Faster RCNN, SSD Mobile Net, SSD ResNet, and YOLO that detects the violator of accidents with the aid of our own developed Rail Obstacle Detection Dataset (RODD). In this perspective, railroad obstacle detection from aerial images has been growing as a trending research topic under artificial intelligence. Also, this manual monitoring is not adequate to halt derailment accidents. But when it comes to the real-time scenario, it turns to fatal work and requires more workers, particularly in a dangerous area. Monitoring these events has been possible with humans working in railways. Count of accident is increasing day by day due to its causes such as boulders on track, trees falling on the gauge, etc. Obstacles on the railway track leading to derailment accidents that cause significant damages to the railway in terms of killed and injuries over the years.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |