This paper provides a report on our solution including model selection, tuning strategy and results obtained for Global Road Damage Detection Challenge. This Big Data Cup Challenge was held as a part of IEEE International Conference on Big Data 2020. We assess single and multi-stage network architectures for object detection and provide a benchmark using popular state-of-the-art open-source PyTorch frameworks like Detectron2 and Yolov5. Data preparation for provided Road Damage training dataset, captured using smartphone camera from Czech, India and Japan is discussed. We studied the effect of training on a per country basis with respect to a single generalizable model. We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual Network (Resnet) and Feature Pyramid Network (FPN) backbone. Additionally, we compare this to a one-stage Yolov5 model with Cross Stage Partial Network (CSPNet) backbone. We show a mean F1 score of 0.542 on Test2 and 0.536 on Test1 datasets using a multi-stage Faster R-CNN model, with Resnet-50 and Resnet-101 backbones respectively. This shows the generalizability of the Resnet-50 model when compared to its more complex counterparts. Experiments were conducted using Google Colab having K80 and a Linux PC with 1080Ti, NVIDIA consumer grade GPU. A PyTorch based Detectron2 code to preprocess, train, test and submit the Avg F1 score to is made available at https://github.com/vishwakarmarhl/rdd2020

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Today's large data centres are the computational hubs of the next generation of IT services. With the advent of dynamic smart cooling and rack level sensing, the need for visual data exploration is growing. If administrators know the rack level thermal state changes and catch problems in real time, energy consumption can be greatly reduced. In this paper, we apply a cell-based spatio-temporal overall view with high-resolution time series to simultaneously analyze complex thermal state changes over time across hundreds of racks. We employ cell-based visualization techniques for trouble shooting and abnormal state detection. These techniques are based on the detection of sensor temperature relations and events to help identify the root causes of problems. In order to optimize the data centre cooling system performance, we derive new non-overlapped scatter plots to visualize the correlations between the temperatures and chiller utilization. All these techniques have been used successfully to monitor various time-critical thermal states in real-world large-scale production data centres and to derive cooling policies. We are starting to embed these visualization techniques into a handheld device to add mobile monitoring capability.

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