Many post-disaster and post-conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of ageing and deteriorating bridges increases, it is necessary to quantify their load characteristics in order to inform maintenance and asset databases. The load carrying capacity and the design load are considered as the main aspects of any civil structures. Human examination can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as a method to estimate the load carrying capacity from crowdsourced images. A convolutional neural network architecture is trained on data from over 6000 bridges, which will benefit future research and applications. We observe significant variations in the dataset (e.g. class interval, image completion, image colour) and quantify their impact on the prediction accuracy, precision, recall and F1 score. Finally, practical optimization is performed by converting multiclass classification into binary classification to achieve a promising field use performance.

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Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using deep learning, is an optimized solution to localize and detect the presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage object detectors whose resultant model not only detects the presence of weapons but also classifies with respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person detection. This research focuses on YOLOv5 (You Look Only Once) family and Faster RCNN family for model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to enhance their speed and performance. YOLOv5 models achieve the highest score of 78% with an inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.